Simple cellular automaton model for traffic breakdown, highway capacity, and synchronized flow.

PubMed

Kerner, Boris S; Klenov, Sergey L; Schreckenberg, Michael

2011-10-01

We present a simple cellular automaton (CA) model for two-lane roads explaining the physics of traffic breakdown, highway capacity, and synchronized flow. The model consists of the rules "acceleration," "deceleration," "randomization," and "motion" of the Nagel-Schreckenberg CA model as well as "overacceleration through lane changing to the faster lane," "comparison of vehicle gap with the synchronization gap," and "speed adaptation within the synchronization gap" of Kerner's three-phase traffic theory. We show that these few rules of the CA model can appropriately simulate fundamental empirical features of traffic breakdown and highway capacity found in traffic data measured over years in different countries, like characteristics of synchronized flow, the existence of the spontaneous and induced breakdowns at the same bottleneck, and associated probabilistic features of traffic breakdown and highway capacity. Single-vehicle data derived in model simulations show that synchronized flow first occurs and then self-maintains due to a spatiotemporal competition between speed adaptation to a slower speed of the preceding vehicle and passing of this slower vehicle. We find that the application of simple dependences of randomization probability and synchronization gap on driving situation allows us to explain the physics of moving synchronized flow patterns and the pinch effect in synchronized flow as observed in real traffic data.

Integrated Display and Simulation for Automatic Dependent Surveillance-Broadcast and Traffic Collision Avoidance System Data Fusion.

PubMed

Wang, Yanran; Xiao, Gang; Dai, Zhouyun

2017-11-13

Automatic Dependent Surveillance-Broadcast (ADS-B) is the direction of airspace surveillance development. Research analyzing the benefits of Traffic Collision Avoidance System (TCAS) and ADS-B data fusion is almost absent. The paper proposes an ADS-B minimum system from ADS-B In and ADS-B Out. In ADS-B In, a fusion model with a variable sampling Variational Bayesian-Interacting Multiple Model (VSVB-IMM) algorithm is proposed for integrated display and an airspace traffic situation display is developed by using ADS-B information. ADS-B Out includes ADS-B Out transmission based on a simulator platform and an Unmanned Aerial Vehicle (UAV) platform. This paper describes the overall implementation of ADS-B minimum system, including theoretical model design, experimental simulation verification, engineering implementation, results analysis, etc. Simulation and implementation results show that the fused system has better performance than each independent subsystem and it can work well in engineering applications.

Complex traffic flow that allows as well as hampers lane-changing intrinsically contains social-dilemma structures

NASA Astrophysics Data System (ADS)

Iwamura, Yoshiro; Tanimoto, Jun

2018-02-01

To investigate an interesting question as to whether or not social dilemma structures can be found in a realistic traffic flow reproduced by a model, we built a new microscopic model in which an intentional driver may try lane-changing to go in front of other vehicles and may hamper others’ lane-changes. Our model consists of twofold parts; cellular automaton emulating a real traffic flow and evolutionary game theory to implement a driver’s decision making-process. Numerical results reveal that a social dilemma like the multi-player chicken game or prisoner’s dilemma game emerges depending on the traffic phase. This finding implies that a social dilemma, which has been investigated by applied mathematics so far, hides behind a traffic flow, which has been explored by fluid dynamics. Highlight - Complex system of traffic flow with consideration of driver’s decision making process is concerned. - A new model dovetailing cellular automaton with game theory is established. - Statistical result from numerical simulations reveals a social dilemma structure underlying traffic flow. - The social dilemma is triggered by a driver’s egocentric actions of lane-changing and hampering other’s lane-change.

Finite size scaling analysis on Nagel-Schreckenberg model for traffic flow

NASA Astrophysics Data System (ADS)

Balouchi, Ashkan; Browne, Dana

2015-03-01

The traffic flow problem as a many-particle non-equilibrium system has caught the interest of physicists for decades. Understanding the traffic flow properties and though obtaining the ability to control the transition from the free-flow phase to the jammed phase plays a critical role in the future world of urging self-driven cars technology. We have studied phase transitions in one-lane traffic flow through the mean velocity, distributions of car spacing, dynamic susceptibility and jam persistence -as candidates for an order parameter- using the Nagel-Schreckenberg model to simulate traffic flow. The length dependent transition has been observed for a range of maximum velocities greater than a certain value. Finite size scaling analysis indicates power-law scaling of these quantities at the onset of the jammed phase.

Do alcohol excise taxes affect traffic accidents? Evidence from Estonia.

PubMed

Saar, Indrek

2015-01-01

This article examines the association between alcohol excise tax rates and alcohol-related traffic accidents in Estonia. Monthly time series of traffic accidents involving drunken motor vehicle drivers from 1998 through 2013 were regressed on real average alcohol excise tax rates while controlling for changes in economic conditions and the traffic environment. Specifically, regression models with autoregressive integrated moving average (ARIMA) errors were estimated in order to deal with serial correlation in residuals. Counterfactual models were also estimated in order to check the robustness of the results, using the level of non-alcohol-related traffic accidents as a dependent variable. A statistically significant (P <.01) strong negative relationship between the real average alcohol excise tax rate and alcohol-related traffic accidents was disclosed under alternative model specifications. For instance, the regression model with ARIMA (0, 1, 1)(0, 1, 1) errors revealed that a 1-unit increase in the tax rate is associated with a 1.6% decrease in the level of accidents per 100,000 population involving drunk motor vehicle drivers. No similar association was found in the cases of counterfactual models for non-alcohol-related traffic accidents. This article indicates that the level of alcohol-related traffic accidents in Estonia has been affected by changes in real average alcohol excise taxes during the period 1998-2013. Therefore, in addition to other measures, the use of alcohol taxation is warranted as a policy instrument in tackling alcohol-related traffic accidents.

Sensor Location Problem Optimization for Traffic Network with Different Spatial Distributions of Traffic Information.

PubMed

Bao, Xu; Li, Haijian; Qin, Lingqiao; Xu, Dongwei; Ran, Bin; Rong, Jian

2016-10-27

To obtain adequate traffic information, the density of traffic sensors should be sufficiently high to cover the entire transportation network. However, deploying sensors densely over the entire network may not be realistic for practical applications due to the budgetary constraints of traffic management agencies. This paper describes several possible spatial distributions of traffic information credibility and proposes corresponding different sensor information credibility functions to describe these spatial distribution properties. A maximum benefit model and its simplified model are proposed to solve the traffic sensor location problem. The relationships between the benefit and the number of sensors are formulated with different sensor information credibility functions. Next, expanding models and algorithms in analytic results are performed. For each case, the maximum benefit, the optimal number and spacing of sensors are obtained and the analytic formulations of the optimal sensor locations are derived as well. Finally, a numerical example is proposed to verify the validity and availability of the proposed models for solving a network sensor location problem. The results show that the optimal number of sensors of segments with different model parameters in an entire freeway network can be calculated. Besides, it can also be concluded that the optimal sensor spacing is independent of end restrictions but dependent on the values of model parameters that represent the physical conditions of sensors and roads.

Sensor Location Problem Optimization for Traffic Network with Different Spatial Distributions of Traffic Information

PubMed Central

Bao, Xu; Li, Haijian; Qin, Lingqiao; Xu, Dongwei; Ran, Bin; Rong, Jian

2016-01-01

To obtain adequate traffic information, the density of traffic sensors should be sufficiently high to cover the entire transportation network. However, deploying sensors densely over the entire network may not be realistic for practical applications due to the budgetary constraints of traffic management agencies. This paper describes several possible spatial distributions of traffic information credibility and proposes corresponding different sensor information credibility functions to describe these spatial distribution properties. A maximum benefit model and its simplified model are proposed to solve the traffic sensor location problem. The relationships between the benefit and the number of sensors are formulated with different sensor information credibility functions. Next, expanding models and algorithms in analytic results are performed. For each case, the maximum benefit, the optimal number and spacing of sensors are obtained and the analytic formulations of the optimal sensor locations are derived as well. Finally, a numerical example is proposed to verify the validity and availability of the proposed models for solving a network sensor location problem. The results show that the optimal number of sensors of segments with different model parameters in an entire freeway network can be calculated. Besides, it can also be concluded that the optimal sensor spacing is independent of end restrictions but dependent on the values of model parameters that represent the physical conditions of sensors and roads. PMID:27801794

Simple cellular automaton model for traffic breakdown, highway capacity, and synchronized flow

NASA Astrophysics Data System (ADS)

Kerner, Boris S.; Klenov, Sergey L.; Schreckenberg, Michael

2011-10-01

We present a simple cellular automaton (CA) model for two-lane roads explaining the physics of traffic breakdown, highway capacity, and synchronized flow. The model consists of the rules “acceleration,” “deceleration,” “randomization,” and “motion” of the Nagel-Schreckenberg CA model as well as “overacceleration through lane changing to the faster lane,” “comparison of vehicle gap with the synchronization gap,” and “speed adaptation within the synchronization gap” of Kerner's three-phase traffic theory. We show that these few rules of the CA model can appropriately simulate fundamental empirical features of traffic breakdown and highway capacity found in traffic data measured over years in different countries, like characteristics of synchronized flow, the existence of the spontaneous and induced breakdowns at the same bottleneck, and associated probabilistic features of traffic breakdown and highway capacity. Single-vehicle data derived in model simulations show that synchronized flow first occurs and then self-maintains due to a spatiotemporal competition between speed adaptation to a slower speed of the preceding vehicle and passing of this slower vehicle. We find that the application of simple dependences of randomization probability and synchronization gap on driving situation allows us to explain the physics of moving synchronized flow patterns and the pinch effect in synchronized flow as observed in real traffic data.

Effects of traffic generation patterns on the robustness of complex networks

NASA Astrophysics Data System (ADS)

Wu, Jiajing; Zeng, Junwen; Chen, Zhenhao; Tse, Chi K.; Chen, Bokui

2018-02-01

Cascading failures in communication networks with heterogeneous node functions are studied in this paper. In such networks, the traffic dynamics are highly dependent on the traffic generation patterns which are in turn determined by the locations of the hosts. The data-packet traffic model is applied to Barabási-Albert scale-free networks to study the cascading failures in such networks and to explore the effects of traffic generation patterns on network robustness. It is found that placing the hosts at high-degree nodes in a network can make the network more robust against both intentional attacks and random failures. It is also shown that the traffic generation pattern plays an important role in network design.

A SPATIOTEMPORAL APPROACH FOR HIGH RESOLUTION TRAFFIC FLOW IMPUTATION

DOE Office of Scientific and Technical Information (OSTI.GOV)

Han, Lee; Chin, Shih-Miao; Hwang, Ho-Ling

Along with the rapid development of Intelligent Transportation Systems (ITS), traffic data collection technologies have been evolving dramatically. The emergence of innovative data collection technologies such as Remote Traffic Microwave Sensor (RTMS), Bluetooth sensor, GPS-based Floating Car method, automated license plate recognition (ALPR) (1), etc., creates an explosion of traffic data, which brings transportation engineering into the new era of Big Data. However, despite the advance of technologies, the missing data issue is still inevitable and has posed great challenges for research such as traffic forecasting, real-time incident detection and management, dynamic route guidance, and massive evacuation optimization, because themore » degree of success of these endeavors depends on the timely availability of relatively complete and reasonably accurate traffic data. A thorough literature review suggests most current imputation models, if not all, focus largely on the temporal nature of the traffic data and fail to consider the fact that traffic stream characteristics at a certain location are closely related to those at neighboring locations and utilize these correlations for data imputation. To this end, this paper presents a Kriging based spatiotemporal data imputation approach that is able to fully utilize the spatiotemporal information underlying in traffic data. Imputation performance of the proposed approach was tested using simulated scenarios and achieved stable imputation accuracy. Moreover, the proposed Kriging imputation model is more flexible compared to current models.« less

Integrated Display and Simulation for Automatic Dependent Surveillance–Broadcast and Traffic Collision Avoidance System Data Fusion

PubMed Central

Wang, Yanran; Xiao, Gang; Dai, Zhouyun

2017-01-01

Automatic Dependent Surveillance–Broadcast (ADS-B) is the direction of airspace surveillance development. Research analyzing the benefits of Traffic Collision Avoidance System (TCAS) and ADS-B data fusion is almost absent. The paper proposes an ADS-B minimum system from ADS-B In and ADS-B Out. In ADS-B In, a fusion model with a variable sampling Variational Bayesian-Interacting Multiple Model (VSVB-IMM) algorithm is proposed for integrated display and an airspace traffic situation display is developed by using ADS-B information. ADS-B Out includes ADS-B Out transmission based on a simulator platform and an Unmanned Aerial Vehicle (UAV) platform. This paper describes the overall implementation of ADS-B minimum system, including theoretical model design, experimental simulation verification, engineering implementation, results analysis, etc. Simulation and implementation results show that the fused system has better performance than each independent subsystem and it can work well in engineering applications. PMID:29137194

Predictability of Road Traffic and Congestion in Urban Areas

PubMed Central

Wang, Jingyuan; Mao, Yu; Li, Jing; Xiong, Zhang; Wang, Wen-Xu

2015-01-01

Mitigating traffic congestion on urban roads, with paramount importance in urban development and reduction of energy consumption and air pollution, depends on our ability to foresee road usage and traffic conditions pertaining to the collective behavior of drivers, raising a significant question: to what degree is road traffic predictable in urban areas? Here we rely on the precise records of daily vehicle mobility based on GPS positioning device installed in taxis to uncover the potential daily predictability of urban traffic patterns. Using the mapping from the degree of congestion on roads into a time series of symbols and measuring its entropy, we find a relatively high daily predictability of traffic conditions despite the absence of any priori knowledge of drivers' origins and destinations and quite different travel patterns between weekdays and weekends. Moreover, we find a counterintuitive dependence of the predictability on travel speed: the road segment associated with intermediate average travel speed is most difficult to be predicted. We also explore the possibility of recovering the traffic condition of an inaccessible segment from its adjacent segments with respect to limited observability. The highly predictable traffic patterns in spite of the heterogeneity of drivers' behaviors and the variability of their origins and destinations enables development of accurate predictive models for eventually devising practical strategies to mitigate urban road congestion. PMID:25849534

Traveling waves in an optimal velocity model of freeway traffic.

PubMed

Berg, P; Woods, A

2001-03-01

Car-following models provide both a tool to describe traffic flow and algorithms for autonomous cruise control systems. Recently developed optimal velocity models contain a relaxation term that assigns a desirable speed to each headway and a response time over which drivers adjust to optimal velocity conditions. These models predict traffic breakdown phenomena analogous to real traffic instabilities. In order to deepen our understanding of these models, in this paper, we examine the transition from a linear stable stream of cars of one headway into a linear stable stream of a second headway. Numerical results of the governing equations identify a range of transition phenomena, including monotonic and oscillating travelling waves and a time- dependent dispersive adjustment wave. However, for certain conditions, we find that the adjustment takes the form of a nonlinear traveling wave from the upstream headway to a third, intermediate headway, followed by either another traveling wave or a dispersive wave further downstream matching the downstream headway. This intermediate value of the headway is selected such that the nonlinear traveling wave is the fastest stable traveling wave which is observed to develop in the numerical calculations. The development of these nonlinear waves, connecting linear stable flows of two different headways, is somewhat reminiscent of stop-start waves in congested flow on freeways. The different types of adjustments are classified in a phase diagram depending on the upstream and downstream headway and the response time of the model. The results have profound consequences for autonomous cruise control systems. For an autocade of both identical and different vehicles, the control system itself may trigger formations of nonlinear, steep wave transitions. Further information is available [Y. Sugiyama, Traffic and Granular Flow (World Scientific, Singapore, 1995), p. 137].

Traveling waves in an optimal velocity model of freeway traffic

NASA Astrophysics Data System (ADS)

Berg, Peter; Woods, Andrew

2001-03-01

Car-following models provide both a tool to describe traffic flow and algorithms for autonomous cruise control systems. Recently developed optimal velocity models contain a relaxation term that assigns a desirable speed to each headway and a response time over which drivers adjust to optimal velocity conditions. These models predict traffic breakdown phenomena analogous to real traffic instabilities. In order to deepen our understanding of these models, in this paper, we examine the transition from a linear stable stream of cars of one headway into a linear stable stream of a second headway. Numerical results of the governing equations identify a range of transition phenomena, including monotonic and oscillating travelling waves and a time- dependent dispersive adjustment wave. However, for certain conditions, we find that the adjustment takes the form of a nonlinear traveling wave from the upstream headway to a third, intermediate headway, followed by either another traveling wave or a dispersive wave further downstream matching the downstream headway. This intermediate value of the headway is selected such that the nonlinear traveling wave is the fastest stable traveling wave which is observed to develop in the numerical calculations. The development of these nonlinear waves, connecting linear stable flows of two different headways, is somewhat reminiscent of stop-start waves in congested flow on freeways. The different types of adjustments are classified in a phase diagram depending on the upstream and downstream headway and the response time of the model. The results have profound consequences for autonomous cruise control systems. For an autocade of both identical and different vehicles, the control system itself may trigger formations of nonlinear, steep wave transitions. Further information is available [Y. Sugiyama, Traffic and Granular Flow (World Scientific, Singapore, 1995), p. 137].

Dynamic route and departure time choice model based on self-adaptive reference point and reinforcement learning

NASA Astrophysics Data System (ADS)

Li, Xue-yan; Li, Xue-mei; Yang, Lingrun; Li, Jing

2018-07-01

Most of the previous studies on dynamic traffic assignment are based on traditional analytical framework, for instance, the idea of Dynamic User Equilibrium has been widely used in depicting both the route choice and the departure time choice. However, some recent studies have demonstrated that the dynamic traffic flow assignment largely depends on travelers' rationality degree, travelers' heterogeneity and what the traffic information the travelers have. In this paper, we develop a new self-adaptive multi agent model to depict travelers' behavior in Dynamic Traffic Assignment. We use Cumulative Prospect Theory with heterogeneous reference points to illustrate travelers' bounded rationality. We use reinforcement-learning model to depict travelers' route and departure time choosing behavior under the condition of imperfect information. We design the evolution rule of travelers' expected arrival time and the algorithm of traffic flow assignment. Compared with the traditional model, the self-adaptive multi agent model we proposed in this paper can effectively help travelers avoid the rush hour. Finally, we report and analyze the effect of travelers' group behavior on the transportation system, and give some insights into the relation between travelers' group behavior and the performance of transportation system.

An Agent-Based Model for Analyzing Control Policies and the Dynamic Service-Time Performance of a Capacity-Constrained Air Traffic Management Facility

NASA Technical Reports Server (NTRS)

Conway, Sheila R.

2006-01-01

Simple agent-based models may be useful for investigating air traffic control strategies as a precursory screening for more costly, higher fidelity simulation. Of concern is the ability of the models to capture the essence of the system and provide insight into system behavior in a timely manner and without breaking the bank. The method is put to the test with the development of a model to address situations where capacity is overburdened and potential for propagation of the resultant delay though later flights is possible via flight dependencies. The resultant model includes primitive representations of principal air traffic system attributes, namely system capacity, demand, airline schedules and strategy, and aircraft capability. It affords a venue to explore their interdependence in a time-dependent, dynamic system simulation. The scope of the research question and the carefully-chosen modeling fidelity did allow for the development of an agent-based model in short order. The model predicted non-linear behavior given certain initial conditions and system control strategies. Additionally, a combination of the model and dimensionless techniques borrowed from fluid systems was demonstrated that can predict the system s dynamic behavior across a wide range of parametric settings.

Air pollution and health risks due to vehicle traffic.

PubMed

Zhang, Kai; Batterman, Stuart

2013-04-15

Traffic congestion increases vehicle emissions and degrades ambient air quality, and recent studies have shown excess morbidity and mortality for drivers, commuters and individuals living near major roadways. Presently, our understanding of the air pollution impacts from congestion on roads is very limited. This study demonstrates an approach to characterize risks of traffic for on- and near-road populations. Simulation modeling was used to estimate on- and near-road NO2 concentrations and health risks for freeway and arterial scenarios attributable to traffic for different traffic volumes during rush hour periods. The modeling used emission factors from two different models (Comprehensive Modal Emissions Model and Motor Vehicle Emissions Factor Model version 6.2), an empirical traffic speed-volume relationship, the California Line Source Dispersion Model, an empirical NO2-NOx relationship, estimated travel time changes during congestion, and concentration-response relationships from the literature, which give emergency doctor visits, hospital admissions and mortality attributed to NO2 exposure. An incremental analysis, which expresses the change in health risks for small increases in traffic volume, showed non-linear effects. For a freeway, "U" shaped trends of incremental risks were predicted for on-road populations, and incremental risks are flat at low traffic volumes for near-road populations. For an arterial road, incremental risks increased sharply for both on- and near-road populations as traffic increased. These patterns result from changes in emission factors, the NO2-NOx relationship, the travel delay for the on-road population, and the extended duration of rush hour for the near-road population. This study suggests that health risks from congestion are potentially significant, and that additional traffic can significantly increase risks, depending on the type of road and other factors. Further, evaluations of risk associated with congestion must consider travel time, the duration of rush-hour, congestion-specific emission estimates, and uncertainties. Copyright © 2013 Elsevier B.V. All rights reserved.

Air pollution and health risks due to vehicle traffic

PubMed Central

Zhang, Kai; Batterman, Stuart

2014-01-01

Traffic congestion increases vehicle emissions and degrades ambient air quality, and recent studies have shown excess morbidity and mortality for drivers, commuters and individuals living near major roadways. Presently, our understanding of the air pollution impacts from congestion on roads is very limited. This study demonstrates an approach to characterize risks of traffic for on- and near-road populations. Simulation modeling was used to estimate on- and near-road NO2 concentrations and health risks for freeway and arterial scenarios attributable to traffic for different traffic volumes during rush hour periods. The modeling used emission factors from two different models (Comprehensive Modal Emissions Model and Motor Vehicle Emissions Factor Model version 6.2), an empirical traffic speed–volume relationship, the California Line Source Dispersion Model, an empirical NO2–NOx relationship, estimated travel time changes during congestion, and concentration–response relationships from the literature, which give emergency doctor visits, hospital admissions and mortality attributed to NO2 exposure. An incremental analysis, which expresses the change in health risks for small increases in traffic volume, showed non-linear effects. For a freeway, “U” shaped trends of incremental risks were predicted for on-road populations, and incremental risks are flat at low traffic volumes for near-road populations. For an arterial road, incremental risks increased sharply for both on- and near-road populations as traffic increased. These patterns result from changes in emission factors, the NO2–NOx relationship, the travel delay for the on-road population, and the extended duration of rush hour for the near-road population. This study suggests that health risks from congestion are potentially significant, and that additional traffic can significantly increase risks, depending on the type of road and other factors. Further, evaluations of risk associated with congestion must consider travel time, the duration of rush-hour, congestion-specific emission estimates, and uncertainties. PMID:23500830

Study on traffic characteristics for a typical expressway on-ramp bottleneck considering various merging behaviors

NASA Astrophysics Data System (ADS)

Sun, Jie; Li, Zhipeng; Sun, Jian

2015-12-01

Recurring bottlenecks at freeway/expressway are considered as the main cause of traffic congestion in urban traffic system while on-ramp bottlenecks are the most significant sites that may result in congestion. In this paper, the traffic bottleneck characteristics for a simple and typical expressway on-ramp are investigated by the means of simulation modeling under the open boundary condition. In simulations, the running behaviors of each vehicle are described by a car-following model with a calibrated optimal velocity function, and lane changing actions at the merging section are modeled by a novel set of rules. We numerically derive the traffic volume of on-ramp bottleneck under different upstream arrival rates of mainline and ramp flows. It is found that the vehicles from the ramp strongly affect the pass of mainline vehicles and the merging ratio changes with the increasing of ramp vehicle, when the arrival rate of mainline flow is greater than a critical value. In addition, we clarify the dependence of the merging ratio of on-ramp bottleneck on the probability of lane changing and the length of the merging section, and some corresponding intelligent control strategies are proposed in actual traffic application.

Did Chile's traffic law reform push police enforcement? Understanding Chile's traffic fatalities and injuries reduction.

PubMed

Nazif-Munoz, José Ignacio; Quesnel-Vallée, Amélie; van den Berg, Axel

2015-06-01

The objective of the current study is to determine to what extent the reduction of Chile's traffic fatalities and injuries during 2000-2012 was related to the police traffic enforcement increment registered after the introduction of its 2005 traffic law reform. A unique dataset with assembled information from public institutions and analyses based on ordinary least square and robust random effects models was carried out. Dependent variables were traffic fatality and severe injury rates per population and vehicle fleet. Independent variables were: (1) presence of new national traffic law; (2) police officers per population; (3) number of traffic tickets per police officer; and (4) interaction effect of number of traffic tickets per police officer with traffic law reform. Oil prices, alcohol consumption, proportion of male population 15-24 years old, unemployment, road infrastructure investment, years' effects and regions' effects represented control variables. Empirical estimates from instrumental variables suggest that the enactment of the traffic law reform in interaction with number of traffic tickets per police officer is significantly associated with a decrease of 8% in traffic fatalities and 7% in severe injuries. Piecewise regression model results for the 2007-2012 period suggest that police traffic enforcement reduced traffic fatalities by 59% and severe injuries by 37%. Findings suggest that traffic law reforms in order to have an effect on both traffic fatality and injury rates reduction require changes in police enforcement practices. Last, this case also illustrates how the diffusion of successful road safety practices globally promoted by WHO and World Bank can be an important influence for enhancing national road safety practices. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Combined Prediction Model of Death Toll for Road Traffic Accidents Based on Independent and Dependent Variables

PubMed Central

Zhong-xiang, Feng; Shi-sheng, Lu; Wei-hua, Zhang; Nan-nan, Zhang

2014-01-01

In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability. PMID:25610454

Combined prediction model of death toll for road traffic accidents based on independent and dependent variables.

PubMed

Feng, Zhong-xiang; Lu, Shi-sheng; Zhang, Wei-hua; Zhang, Nan-nan

2014-01-01

In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability.

Microscopic Statistical Characterisation of the Congested Traffic Flow and Some Salient Empirical Features

NASA Astrophysics Data System (ADS)

Yang, Bo; Yoon, Ji Wei; Monterola, Christopher

We present large scale, detailed analysis of the microscopic empirical data of the congested traffic flow, focusing on the non-linear interactions between the components of the many-body traffic system. By implementing a systematic procedure that averages over relatively unimportant factors, we extract the effective dependence of the acceleration on the gap between the vehicles, velocity and relative velocity. Such relationship is characterised not just by a few vehicles but the traffic system as a whole. Several interesting features of the detailed vehicle-to-vehicle interactions are revealed, including the stochastic distribution of the human responses, relative importance of the non-linear terms in different density regimes, symmetric response to the relative velocity, and the insensitivity of the acceleration to the velocity within a certain gap and velocity range. The latter leads to a multitude of steady-states without a fundamental diagram. The empirically constructed functional dependence of the acceleration on the important dynamical quantities not only gives the detailed collective driving behaviours of the traffic system, it also serves as the fundamental reference for the validations of the deterministic and stochastic microscopic traffic models in the literature.

Resonance, criticality, and emergence in city traffic investigated in cellular automaton models.

PubMed

Varas, A; Cornejo, M D; Toledo, B A; Muñoz, V; Rogan, J; Zarama, R; Valdivia, J A

2009-11-01

The complex behavior that occurs when traffic lights are synchronized is studied for a row of interacting cars. The system is modeled through a cellular automaton. Two strategies are considered: all lights in phase and a "green wave" with a propagating green signal. It is found that the mean velocity near the resonant condition follows a critical scaling law. For the green wave, it is shown that the mean velocity scaling law holds even for random separation between traffic lights and is not dependent on the density. This independence on car density is broken when random perturbations are considered in the car velocity. Random velocity perturbations also have the effect of leading the system to an emergent state, where cars move in clusters, but with an average velocity which is independent of traffic light switching for large injection rates.

Outside and inside noise exposure in urban and suburban areas

Treesearch

Dwight E. Bishop; Myles A. Simpson

1977-01-01

In urban and suburban areas of the United States (away from major airports), the outdoor noise environment usually depends strongly on local vehicular traffic. By relating traffic flow to population density, a model of outdoor noise exposure has been developed for estimating the cumulative 24-hour noise exposure based upon the population density of the area. This noise...

New full velocity difference model considering the driver’s heterogeneity of the disturbance risk preference for car-following theory

NASA Astrophysics Data System (ADS)

Zeng, You-Zhi; Zhang, Ning

2016-12-01

This paper proposes a new full velocity difference model considering the driver’s heterogeneity of the disturbance risk preference for car-following theory to investigate the effects of the driver’s heterogeneity of the disturbance risk preference on traffic flow instability when the driver reacts to the relative velocity. We obtain traffic flow instability condition and the calculation method of the unstable region headway range and the probability of traffic congestion caused by a small disturbance. The analysis shows that has important effects the driver’s heterogeneity of the disturbance risk preference on traffic flow instability: (1) traffic flow instability is independent of the absolute size of the driver’s disturbance risk preference coefficient and depends on the ratio of the preceding vehicle driver’s disturbance risk preference coefficient to the following vehicle driver’s disturbance risk preference coefficient; (2) the smaller the ratio of the preceding vehicle driver’s disturbance risk preference coefficient to the following vehicle driver’s disturbance risk preference coefficient, the smaller traffic flow instability and vice versa. It provides some viable ideas to suppress traffic congestion.

A Comparison of Exposure Metrics for Traffic-Related Air Pollutants: Application to Epidemiology Studies in Detroit, Michigan

PubMed Central

Batterman, Stuart; Burke, Janet; Isakov, Vlad; Lewis, Toby; Mukherjee, Bhramar; Robins, Thomas

2014-01-01

Vehicles are major sources of air pollutant emissions, and individuals living near large roads endure high exposures and health risks associated with traffic-related air pollutants. Air pollution epidemiology, health risk, environmental justice, and transportation planning studies would all benefit from an improved understanding of the key information and metrics needed to assess exposures, as well as the strengths and limitations of alternate exposure metrics. This study develops and evaluates several metrics for characterizing exposure to traffic-related air pollutants for the 218 residential locations of participants in the NEXUS epidemiology study conducted in Detroit (MI, USA). Exposure metrics included proximity to major roads, traffic volume, vehicle mix, traffic density, vehicle exhaust emissions density, and pollutant concentrations predicted by dispersion models. Results presented for each metric include comparisons of exposure distributions, spatial variability, intraclass correlation, concordance and discordance rates, and overall strengths and limitations. While showing some agreement, the simple categorical and proximity classifications (e.g., high diesel/low diesel traffic roads and distance from major roads) do not reflect the range and overlap of exposures seen in the other metrics. Information provided by the traffic density metric, defined as the number of kilometers traveled (VKT) per day within a 300 m buffer around each home, was reasonably consistent with the more sophisticated metrics. Dispersion modeling provided spatially- and temporally-resolved concentrations, along with apportionments that separated concentrations due to traffic emissions and other sources. While several of the exposure metrics showed broad agreement, including traffic density, emissions density and modeled concentrations, these alternatives still produced exposure classifications that differed for a substantial fraction of study participants, e.g., from 20% to 50% of homes, depending on the metric, would be incorrectly classified into “low”, “medium” or “high” traffic exposure classes. These and other results suggest the potential for exposure misclassification and the need for refined and validated exposure metrics. While data and computational demands for dispersion modeling of traffic emissions are non-trivial concerns, once established, dispersion modeling systems can provide exposure information for both on- and near-road environments that would benefit future traffic-related assessments. PMID:25226412

Space evolution model and empirical analysis of an urban public transport network

NASA Astrophysics Data System (ADS)

Sui, Yi; Shao, Feng-jing; Sun, Ren-cheng; Li, Shu-jing

2012-07-01

This study explores the space evolution of an urban public transport network, using empirical evidence and a simulation model validated on that data. Public transport patterns primarily depend on traffic spatial-distribution, demands of passengers and expected utility of investors. Evolution is an iterative process of satisfying the needs of passengers and investors based on a given traffic spatial-distribution. The temporal change of urban public transport network is evaluated both using topological measures and spatial ones. The simulation model is validated using empirical data from nine big cities in China. Statistical analyses on topological and spatial attributes suggest that an evolution network with traffic demands characterized by power-law numerical values which distribute in a mode of concentric circles tallies well with these nine cities.

Path Flow Estimation Using Time Varying Coefficient State Space Model

NASA Astrophysics Data System (ADS)

Jou, Yow-Jen; Lan, Chien-Lun

2009-08-01

The dynamic path flow information is very crucial in the field of transportation operation and management, i.e., dynamic traffic assignment, scheduling plan, and signal timing. Time-dependent path information, which is important in many aspects, is nearly impossible to be obtained. Consequently, researchers have been seeking estimation methods for deriving valuable path flow information from less expensive traffic data, primarily link traffic counts of surveillance systems. This investigation considers a path flow estimation problem involving the time varying coefficient state space model, Gibbs sampler, and Kalman filter. Numerical examples with part of a real network of the Taipei Mass Rapid Transit with real O-D matrices is demonstrated to address the accuracy of proposed model. Results of this study show that this time-varying coefficient state space model is very effective in the estimation of path flow compared to time-invariant model.

Data mining of tree-based models to analyze freeway accident frequency.

PubMed

Chang, Li-Yen; Chen, Wen-Chieh

2005-01-01

Statistical models, such as Poisson or negative binomial regression models, have been employed to analyze vehicle accident frequency for many years. However, these models have their own model assumptions and pre-defined underlying relationship between dependent and independent variables. If these assumptions are violated, the model could lead to erroneous estimation of accident likelihood. Classification and Regression Tree (CART), one of the most widely applied data mining techniques, has been commonly employed in business administration, industry, and engineering. CART does not require any pre-defined underlying relationship between target (dependent) variable and predictors (independent variables) and has been shown to be a powerful tool, particularly for dealing with prediction and classification problems. This study collected the 2001-2002 accident data of National Freeway 1 in Taiwan. A CART model and a negative binomial regression model were developed to establish the empirical relationship between traffic accidents and highway geometric variables, traffic characteristics, and environmental factors. The CART findings indicated that the average daily traffic volume and precipitation variables were the key determinants for freeway accident frequencies. By comparing the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies. By comparing the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies.

Effect of velocity-dependent friction on multiple-vehicle collisions in traffic flow

NASA Astrophysics Data System (ADS)

Nagatani, Takashi

2017-01-01

We present the dynamic model for the multiple-vehicle collisions to take into account the velocity-dependent friction force. We study the effect of the velocity-dependent friction on the chain-reaction crash on a road. In the traffic situation, drivers brake according to taillights of the forward vehicle and the friction force depends highly on the vehicular speed. The first crash may induce more collisions. We investigate whether or not the first collision induces the multiple-vehicle collisions, numerically and analytically. The dynamic transitions occur from no collisions, through a single collision and double collisions, to multiple collisions with decreasing the headway. We explore the effect of the velocity-dependent friction on the dynamic transitions and the region maps in the multiple-vehicle collisions.

Spatiotemporal Context Awareness for Urban Traffic Modeling and Prediction: Sparse Representation Based Variable Selection.

PubMed

Yang, Su; Shi, Shixiong; Hu, Xiaobing; Wang, Minjie

2015-01-01

Spatial-temporal correlations among the data play an important role in traffic flow prediction. Correspondingly, traffic modeling and prediction based on big data analytics emerges due to the city-scale interactions among traffic flows. A new methodology based on sparse representation is proposed to reveal the spatial-temporal dependencies among traffic flows so as to simplify the correlations among traffic data for the prediction task at a given sensor. Three important findings are observed in the experiments: (1) Only traffic flows immediately prior to the present time affect the formation of current traffic flows, which implies the possibility to reduce the traditional high-order predictors into an 1-order model. (2) The spatial context relevant to a given prediction task is more complex than what is assumed to exist locally and can spread out to the whole city. (3) The spatial context varies with the target sensor undergoing prediction and enlarges with the increment of time lag for prediction. Because the scope of human mobility is subject to travel time, identifying the varying spatial context against time lag is crucial for prediction. Since sparse representation can capture the varying spatial context to adapt to the prediction task, it outperforms the traditional methods the inputs of which are confined as the data from a fixed number of nearby sensors. As the spatial-temporal context for any prediction task is fully detected from the traffic data in an automated manner, where no additional information regarding network topology is needed, it has good scalability to be applicable to large-scale networks.

Spatiotemporal Context Awareness for Urban Traffic Modeling and Prediction: Sparse Representation Based Variable Selection

PubMed Central

Yang, Su; Shi, Shixiong; Hu, Xiaobing; Wang, Minjie

2015-01-01

Spatial-temporal correlations among the data play an important role in traffic flow prediction. Correspondingly, traffic modeling and prediction based on big data analytics emerges due to the city-scale interactions among traffic flows. A new methodology based on sparse representation is proposed to reveal the spatial-temporal dependencies among traffic flows so as to simplify the correlations among traffic data for the prediction task at a given sensor. Three important findings are observed in the experiments: (1) Only traffic flows immediately prior to the present time affect the formation of current traffic flows, which implies the possibility to reduce the traditional high-order predictors into an 1-order model. (2) The spatial context relevant to a given prediction task is more complex than what is assumed to exist locally and can spread out to the whole city. (3) The spatial context varies with the target sensor undergoing prediction and enlarges with the increment of time lag for prediction. Because the scope of human mobility is subject to travel time, identifying the varying spatial context against time lag is crucial for prediction. Since sparse representation can capture the varying spatial context to adapt to the prediction task, it outperforms the traditional methods the inputs of which are confined as the data from a fixed number of nearby sensors. As the spatial-temporal context for any prediction task is fully detected from the traffic data in an automated manner, where no additional information regarding network topology is needed, it has good scalability to be applicable to large-scale networks. PMID:26496370

A Space-Time Network-Based Modeling Framework for Dynamic Unmanned Aerial Vehicle Routing in Traffic Incident Monitoring Applications

PubMed Central

Zhang, Jisheng; Jia, Limin; Niu, Shuyun; Zhang, Fan; Tong, Lu; Zhou, Xuesong

2015-01-01

It is essential for transportation management centers to equip and manage a network of fixed and mobile sensors in order to quickly detect traffic incidents and further monitor the related impact areas, especially for high-impact accidents with dramatic traffic congestion propagation. As emerging small Unmanned Aerial Vehicles (UAVs) start to have a more flexible regulation environment, it is critically important to fully explore the potential for of using UAVs for monitoring recurring and non-recurring traffic conditions and special events on transportation networks. This paper presents a space-time network- based modeling framework for integrated fixed and mobile sensor networks, in order to provide a rapid and systematic road traffic monitoring mechanism. By constructing a discretized space-time network to characterize not only the speed for UAVs but also the time-sensitive impact areas of traffic congestion, we formulate the problem as a linear integer programming model to minimize the detection delay cost and operational cost, subject to feasible flying route constraints. A Lagrangian relaxation solution framework is developed to decompose the original complex problem into a series of computationally efficient time-dependent and least cost path finding sub-problems. Several examples are used to demonstrate the results of proposed models in UAVs’ route planning for small and medium-scale networks. PMID:26076404

Highway traffic noise prediction based on GIS

NASA Astrophysics Data System (ADS)

Zhao, Jianghua; Qin, Qiming

2014-05-01

Before building a new road, we need to predict the traffic noise generated by vehicles. Traditional traffic noise prediction methods are based on certain locations and they are not only time-consuming, high cost, but also cannot be visualized. Geographical Information System (GIS) can not only solve the problem of manual data processing, but also can get noise values at any point. The paper selected a road segment from Wenxi to Heyang. According to the geographical overview of the study area and the comparison between several models, we combine the JTG B03-2006 model and the HJ2.4-2009 model to predict the traffic noise depending on the circumstances. Finally, we interpolate the noise values at each prediction point and then generate contours of noise. By overlaying the village data on the noise contour layer, we can get the thematic maps. The use of GIS for road traffic noise prediction greatly facilitates the decision-makers because of GIS spatial analysis function and visualization capabilities. We can clearly see the districts where noise are excessive, and thus it becomes convenient to optimize the road line and take noise reduction measures such as installing sound barriers and relocating villages and so on.

Effect of signals on two-route traffic system with real-time information

NASA Astrophysics Data System (ADS)

Tobita, Kazuhiro; Nagatani, Takashi

2012-12-01

We study the effect of signals on the vehicular traffic in the two-route system at the tour-time feedback strategy where the vehicles move ahead through a series of signals. The Nagel-Schreckenberg model is applied to the vehicular motion. The traffic signals are controlled by both cycle time and split. The tour times on two routes fluctuate periodically and alternately. The period increases with decreasing the split. Also, the tour time on each route varies with time by synchronizing with the density. The dependences of tour times and densities on both split and cycle time are clarified.

Assessment of methods for simplified traffic noise mapping of small cities: Casework of the city of Valdivia, Chile.

PubMed

Bastián-Monarca, Nicolás A; Suárez, Enrique; Arenas, Jorge P

2016-04-15

In many countries such as Chile, there is scarce official information for generating accurate noise maps. Therefore, specific simplification methods are becoming a real need for the acoustic community in developing countries. Thus, the main purpose of this work was to evaluate and apply simplified methods to generate a cost-effective traffic noise map of a small city of Chile. The experimental design involved the simplification of the cartographic information on buildings by clustering the households within a block, and the classification of the vehicular traffic flows into categories to generate an inexpensive noise map. The streets have been classified according to the official road classification of the country. Segregation of vehicles from light, heavy and motorbikes is made to account for traffic flow. In addition, a number of road traffic noise models were compared with noise measurements and consequently the road traffic model RLS-90 was chosen to generate the noise map of the city using the Computer Aided Noise Abatement (CadnaA) software. It was observed a direct dependence between noise levels and traffic flow versus each category of street used. The methodology developed in this study appears to be convenient in developing countries to obtain accurate approximations to develop inexpensive traffic noise maps. Copyright © 2016 Elsevier B.V. All rights reserved.

Development of a time-dependent hurricane evacuation model for the New Orleans area : research project capsule.

DOT National Transportation Integrated Search

2008-08-01

Current hurricane evacuation transportation modeling uses an approach fashioned after the : traditional four-step procedure applied in urban transportation planning. One of the limiting : features of this approach is that it models traffic in a stati...

Integrated optimisation technique based on computer-aided capacity and safety evaluation for managing downstream lane-drop merging area of signalised junctions

NASA Astrophysics Data System (ADS)

Chen, CHAI; Yiik Diew, WONG

2017-02-01

This study provides an integrated strategy, encompassing microscopic simulation, safety assessment, and multi-attribute decision-making, to optimize traffic performance at downstream merging area of signalized intersections. A Fuzzy Cellular Automata (FCA) model is developed to replicate microscopic movement and merging behavior. Based on simulation experiment, the proposed FCA approach is able to provide capacity and safety evaluation of different traffic scenarios. The results are then evaluated through data envelopment analysis (DEA) and analytic hierarchy process (AHP). Optimized geometric layout and control strategies are then suggested for various traffic conditions. An optimal lane-drop distance that is dependent on traffic volume and speed limit can thus be established at the downstream merging area.

Modeling hurricane evacuation traffic : development of a time-dependent hurricane evacuation demand model.

DOT National Transportation Integrated Search

2006-04-01

Little attention has been given to estimating dynamic travel demand in transportation planning in the past. However, when factors influencing travel are changing significantly over time such as with an approaching hurricane - dynamic demand and t...

Impact of aggregation on scaling behavior of Internet backbone traffic

NASA Astrophysics Data System (ADS)

Zhang, Zhi-Li; Ribeiro, Vinay J.; Moon, Sue B.; Diot, Christophe

2002-07-01

We study the impact of aggregation on the scaling behavior of Internet backbone tra ffic, based on traces collected from OC3 and OC12 links in a tier-1 ISP. We make two striking observations regarding the sub-second small time scaling behaviors of Internet backbone traffic: 1) for a majority of these traces, the Hurst parameters at small time scales (1ms - 100ms) are fairly close to 0.5. Hence the traffic at these time scales are nearly uncorrelated; 2) the scaling behaviors at small time scales are link-dependent, and stay fairly invariant over changing utilization and time. To understand the scaling behavior of network traffic, we develop analytical models and employ them to demonstrate how traffic composition -- aggregation of traffic with different characteristics -- affects the small-time scalings of network traffic. The degree of aggregation and burst correlation structure are two major factors in traffic composition. Our trace-based data analysis confirms this. Furthermore, we discover that traffic composition on a backbone link stays fairly consistent over time and changing utilization, which we believe is the cause for the invariant small-time scalings we observe in the traces.

Modeling pedestrian gap crossing index under mixed traffic condition.

PubMed

Naser, Mohamed M; Zulkiple, Adnan; Al Bargi, Walid A; Khalifa, Nasradeen A; Daniel, Basil David

2017-12-01

There are a variety of challenges faced by pedestrians when they walk along and attempt to cross a road, as the most recorded accidents occur during this time. Pedestrians of all types, including both sexes with numerous aging groups, are always subjected to risk and are characterized as the most exposed road users. The increased demand for better traffic management strategies to reduce the risks at intersections, improve quality traffic management, traffic volume, and longer cycle time has further increased concerns over the past decade. This paper aims to develop a sustainable pedestrian gap crossing index model based on traffic flow density. It focusses on the gaps accepted by pedestrians and their decision for street crossing, where (Log-Gap) logarithm of accepted gaps was used to optimize the result of a model for gap crossing behavior. Through a review of extant literature, 15 influential variables were extracted for further empirical analysis. Subsequently, data from the observation at an uncontrolled mid-block in Jalan Ampang in Kuala Lumpur, Malaysia was gathered and Multiple Linear Regression (MLR) and Binary Logit Model (BLM) techniques were employed to analyze the results. From the results, different pedestrian behavioral characteristics were considered for a minimum gap size model, out of which only a few (four) variables could explain the pedestrian road crossing behavior while the remaining variables have an insignificant effect. Among the different variables, age, rolling gap, vehicle type, and crossing were the most influential variables. The study concludes that pedestrians' decision to cross the street depends on the pedestrian age, rolling gap, vehicle type, and size of traffic gap before crossing. The inferences from these models will be useful to increase pedestrian safety and performance evaluation of uncontrolled midblock road crossings in developing countries. Copyright © 2017 National Safety Council and Elsevier Ltd. All rights reserved.

Active transportation: do current traffic safety policies protect non-motorists?

PubMed

Mader, Emily M; Zick, Cathleen D

2014-06-01

This study investigated the impact that state traffic safety regulations have on non-motorist fatality rates. Data obtained from the National Highway Traffic Safety Administration (NHTSA), the Federal Highway Administration (FHWA), and the National Institute on Alcohol Abuse and Alcoholism (NIAAA) were analyzed through a pooled time series cross-sectional model using fixed effects regression for all 50 states from 1999 to 2009. Two dependent variables were used in separate models measuring annual state non-motorist fatalities per million population, and the natural log of state non-motorist fatalities. Independent variables measuring traffic policies included state expenditures for highway law enforcement and safety per capita; driver cell phone use regulations; graduated driver license regulations; driver blood alcohol concentration regulations; bike helmet regulations; and seat belt regulations. Other control variables included percent of all vehicle miles driven that are urban and mean per capita alcohol consumption per year. Non-motorist traffic safety was positively impacted by state highway law enforcement and safety expenditures per capita, with a decrease in non-motorist fatalities occurring with increased spending. Per capita consumption of alcohol also influenced non-motorist fatalities, with higher non-motorist fatalities occurring with higher per capita consumption of alcohol. Other traffic safety covariates did not appear to have a significant impact on non-motorist fatality rates in the models. Our research suggests that increased expenditures on state highway and traffic safety and the initiation/expansion of programs targeted at curbing both driver and non-motorist intoxication are a starting point for the implementation of traffic safety policies that reduce risks for non-motorists. Copyright © 2014 Elsevier Ltd. All rights reserved.

Packet Traffic Dynamics Near Onset of Congestion in Data Communication Network Model

NASA Astrophysics Data System (ADS)

Lawniczak, A. T.; Tang, X.

2006-05-01

The dominant technology of data communication networks is the Packet Switching Network (PSN). It is a complex technology organized as various hierarchical layers according to the International Standard Organization (ISO) Open Systems Interconnect (OSI) Reference Model. The Network Layer of the ISO OSI Reference Model is responsible for delivering packets from their sources to their destinations and for dealing with congestion if it arises in a network. Thus, we focus on this layer and present an abstraction of the Network Layer of the ISO OSI Reference Model. Using this abstraction we investigate how onset of traffic congestion is affected for various routing algorithms by changes in network connection topology. We study how aggregate measures of network performance depend on network connection topology and routing. We explore packets traffic spatio-temporal dynamics near the phase transition point from free flow to congestion for various network connection topologies and routing algorithms. We consider static and adaptive routings. We present selected simulation results.

Quantifying the impact of adaptive traffic control systems on crash frequency and severity: Evidence from Oakland County, Michigan.

PubMed

Fink, Joshua; Kwigizile, Valerian; Oh, Jun-Seok

2016-06-01

Despite seeing widespread usage worldwide, adaptive traffic control systems have experienced relatively little use in the United States. Of the systems used, the Sydney Coordinated Adaptive Traffic System (SCATS) is the most popular in America. Safety benefits of these systems are not as well understood nor as commonly documented. This study investigates the safety benefits of adaptive traffic control systems by using the large SCATS-based system in Oakland County, MI known as FAST-TRAC. This study uses data from FAST-TRAC-controlled intersections in Oakland County and compares a wide variety of geometric, traffic, and crash characteristics to similar intersections in metropolitan areas elsewhere in Michigan. Data from 498 signalized intersections are used to conduct a cross-sectional analysis. Negative binomial models are used to estimate models for three dependent crash variables. Multinomial logit models are used to estimate an injury severity model. A variable tracking the presence of FAST-TRAC controllers at intersections is used in all models to determine if a SCATS-based system has an impact on crash occurrences or crash severity. Estimates show that the presence of SCATS-based controllers at intersections is likely to reduce angle crashes by up to 19.3%. Severity results show a statistically significant increase in non-serious injuries, but not a significant reduction in incapacitating injuries or fatal accidents. Copyright © 2016 Elsevier Ltd and National Safety Council. All rights reserved.

Intraflagellar transport particle size scales inversely with flagellar length: revisiting the balance-point length control model.

PubMed

Engel, Benjamin D; Ludington, William B; Marshall, Wallace F

2009-10-05

The assembly and maintenance of eukaryotic flagella are regulated by intraflagellar transport (IFT), the bidirectional traffic of IFT particles (recently renamed IFT trains) within the flagellum. We previously proposed the balance-point length control model, which predicted that the frequency of train transport should decrease as a function of flagellar length, thus modulating the length-dependent flagellar assembly rate. However, this model was challenged by the differential interference contrast microscopy observation that IFT frequency is length independent. Using total internal reflection fluorescence microscopy to quantify protein traffic during the regeneration of Chlamydomonas reinhardtii flagella, we determined that anterograde IFT trains in short flagella are composed of more kinesin-associated protein and IFT27 proteins than trains in long flagella. This length-dependent remodeling of train size is consistent with the kinetics of flagellar regeneration and supports a revised balance-point model of flagellar length control in which the size of anterograde IFT trains tunes the rate of flagellar assembly.

Congestion transition in air traffic networks.

PubMed

Monechi, Bernardo; Servedio, Vito D P; Loreto, Vittorio

2015-01-01

Air Transportation represents a very interesting example of a complex techno-social system whose importance has considerably grown in time and whose management requires a careful understanding of the subtle interplay between technological infrastructure and human behavior. Despite the competition with other transportation systems, a growth of air traffic is still foreseen in Europe for the next years. The increase of traffic load could bring the current Air Traffic Network above its capacity limits so that safety standards and performances might not be guaranteed anymore. Lacking the possibility of a direct investigation of this scenario, we resort to computer simulations in order to quantify the disruptive potential of an increase in traffic load. To this end we model the Air Transportation system as a complex dynamical network of flights controlled by humans who have to solve potentially dangerous conflicts by redirecting aircraft trajectories. The model is driven and validated through historical data of flight schedules in a European national airspace. While correctly reproducing actual statistics of the Air Transportation system, e.g., the distribution of delays, the model allows for theoretical predictions. Upon an increase of the traffic load injected in the system, the model predicts a transition from a phase in which all conflicts can be successfully resolved, to a phase in which many conflicts cannot be resolved anymore. We highlight how the current flight density of the Air Transportation system is well below the transition, provided that controllers make use of a special re-routing procedure. While the congestion transition displays a universal scaling behavior, its threshold depends on the conflict solving strategy adopted. Finally, the generality of the modeling scheme introduced makes it a flexible general tool to simulate and control Air Transportation systems in realistic and synthetic scenarios.

A link between mitotic entry and membrane growth suggests a novel model for cell size control

PubMed Central

Anastasia, Steph D.; Nguyen, Duy Linh; Thai, Vu; Meloy, Melissa; MacDonough, Tracy

2012-01-01

Addition of new membrane to the cell surface by membrane trafficking is necessary for cell growth. In this paper, we report that blocking membrane traffic causes a mitotic checkpoint arrest via Wee1-dependent inhibitory phosphorylation of Cdk1. Checkpoint signals are relayed by the Rho1 GTPase, protein kinase C (Pkc1), and a specific form of protein phosphatase 2A (PP2ACdc55). Signaling via this pathway is dependent on membrane traffic and appears to increase gradually during polar bud growth. We hypothesize that delivery of vesicles to the site of bud growth generates a signal that is proportional to the extent of polarized membrane growth and that the strength of the signal is read by downstream components to determine when sufficient growth has occurred for initiation of mitosis. Growth-dependent signaling could explain how membrane growth is integrated with cell cycle progression. It could also control both cell size and morphogenesis, thereby reconciling divergent models for mitotic checkpoint function. PMID:22451696

A link between mitotic entry and membrane growth suggests a novel model for cell size control.

PubMed

Anastasia, Steph D; Nguyen, Duy Linh; Thai, Vu; Meloy, Melissa; MacDonough, Tracy; Kellogg, Douglas R

2012-04-02

Addition of new membrane to the cell surface by membrane trafficking is necessary for cell growth. In this paper, we report that blocking membrane traffic causes a mitotic checkpoint arrest via Wee1-dependent inhibitory phosphorylation of Cdk1. Checkpoint signals are relayed by the Rho1 GTPase, protein kinase C (Pkc1), and a specific form of protein phosphatase 2A (PP2A(Cdc55)). Signaling via this pathway is dependent on membrane traffic and appears to increase gradually during polar bud growth. We hypothesize that delivery of vesicles to the site of bud growth generates a signal that is proportional to the extent of polarized membrane growth and that the strength of the signal is read by downstream components to determine when sufficient growth has occurred for initiation of mitosis. Growth-dependent signaling could explain how membrane growth is integrated with cell cycle progression. It could also control both cell size and morphogenesis, thereby reconciling divergent models for mitotic checkpoint function.

Development and Evaluation of an Airborne Separation Assurance System for Autonomous Aircraft Operations

NASA Technical Reports Server (NTRS)

Barhydt, Richard; Palmer, Michael T.; Eischeid, Todd M.

2004-01-01

NASA Langley Research Center is developing an Autonomous Operations Planner (AOP) that functions as an Airborne Separation Assurance System for autonomous flight operations. This development effort supports NASA s Distributed Air-Ground Traffic Management (DAG-TM) operational concept, designed to significantly increase capacity of the national airspace system, while maintaining safety. Autonomous aircraft pilots use the AOP to maintain traffic separation from other autonomous aircraft and managed aircraft flying under today's Instrument Flight Rules, while maintaining traffic flow management constraints assigned by Air Traffic Service Providers. AOP is designed to facilitate eventual implementation through careful modeling of its operational environment, interfaces with other aircraft systems and data links, and conformance with established flight deck conventions and human factors guidelines. AOP uses currently available or anticipated data exchanged over modeled Arinc 429 data buses and an Automatic Dependent Surveillance Broadcast 1090 MHz link. It provides pilots with conflict detection, prevention, and resolution functions and works with the Flight Management System to maintain assigned traffic flow management constraints. The AOP design has been enhanced over the course of several experiments conducted at NASA Langley and is being prepared for an upcoming Joint Air/Ground Simulation with NASA Ames Research Center.

Stability and Responsiveness in a Self-Organized Living Architecture

PubMed Central

Garnier, Simon; Murphy, Tucker; Lutz, Matthew; Hurme, Edward; Leblanc, Simon; Couzin, Iain D.

2013-01-01

Robustness and adaptability are central to the functioning of biological systems, from gene networks to animal societies. Yet the mechanisms by which living organisms achieve both stability to perturbations and sensitivity to input are poorly understood. Here, we present an integrated study of a living architecture in which army ants interconnect their bodies to span gaps. We demonstrate that these self-assembled bridges are a highly effective means of maintaining traffic flow over unpredictable terrain. The individual-level rules responsible depend only on locally-estimated traffic intensity and the number of neighbours to which ants are attached within the structure. We employ a parameterized computational model to reveal that bridges are tuned to be maximally stable in the face of regular, periodic fluctuations in traffic. However analysis of the model also suggests that interactions among ants give rise to feedback processes that result in bridges being highly responsive to sudden interruptions in traffic. Subsequent field experiments confirm this prediction and thus the dual nature of stability and flexibility in living bridges. Our study demonstrates the importance of robust and adaptive modular architecture to efficient traffic organisation and reveals general principles regarding the regulation of form in biological self-assemblies. PMID:23555219

78 FR 40263 - Agency Information Collection Activities: Requests for Comments; Clearance of Renewed Approval of...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-03

... Dependent Surveillance--Broadcast (ADS-B) Out Performance Requirements to Support Air Traffic Control (ATC... Dependent Surveillance--Broadcast (ADS-B) Equipage Mandate To Support Air Traffic Control Service'' (75 FR... Surveillance--Broadcast (ADS-B) Out Performance Requirements to Support Air Traffic Control (ATC) Service Form...

A hierarchical framework for air traffic control

NASA Astrophysics Data System (ADS)

Roy, Kaushik

Air travel in recent years has been plagued by record delays, with over $8 billion in direct operating costs being attributed to 100 million flight delay minutes in 2007. Major contributing factors to delay include weather, congestion, and aging infrastructure; the Next Generation Air Transportation System (NextGen) aims to alleviate these delays through an upgrade of the air traffic control system. Changes to large-scale networked systems such as air traffic control are complicated by the need for coordinated solutions over disparate temporal and spatial scales. Individual air traffic controllers must ensure aircraft maintain safe separation locally with a time horizon of seconds to minutes, whereas regional plans are formulated to efficiently route flows of aircraft around weather and congestion on the order of every hour. More efficient control algorithms that provide a coordinated solution are required to safely handle a larger number of aircraft in a fixed amount of airspace. Improved estimation algorithms are also needed to provide accurate aircraft state information and situational awareness for human controllers. A hierarchical framework is developed to simultaneously solve the sometimes conflicting goals of regional efficiency and local safety. Careful attention is given in defining the interactions between the layers of this hierarchy. In this way, solutions to individual air traffic problems can be targeted and implemented as needed. First, the regional traffic flow management problem is posed as an optimization problem and shown to be NP-Hard. Approximation methods based on aggregate flow models are developed to enable real-time implementation of algorithms that reduce the impact of congestion and adverse weather. Second, the local trajectory design problem is solved using a novel slot-based sector model. This model is used to analyze sector capacity under varying traffic patterns, providing a more comprehensive understanding of how increased automation in NextGen will affect the overall performance of air traffic control. The dissertation also provides solutions to several key estimation problems that support corresponding control tasks. Throughout the development of these estimation algorithms, aircraft motion is modeled using hybrid systems, which encapsulate both the discrete flight mode of an aircraft and the evolution of continuous states such as position and velocity. The target-tracking problem is posed as one of hybrid state estimation, and two new algorithms are developed to exploit structure specific to aircraft motion, especially near airports. First, discrete mode evolution is modeled using state-dependent transitions, in which the likelihood of changing flight modes is dependent on aircraft state. Second, an estimator is designed for systems with limited mode changes, including arrival aircraft. Improved target tracking facilitates increased safety in collision avoidance and trajectory design problems. A multiple-target tracking and identity management algorithm is developed to improve situational awareness for controllers about multiple maneuvering targets in a congested region. Finally, tracking algorithms are extended to predict aircraft landing times; estimated time of arrival prediction is one example of important decision support information for air traffic control.

Green-wave control of an unbalanced two-route traffic system with signals

NASA Astrophysics Data System (ADS)

Tobita, Kazuhiro; Nagatani, Takashi

2013-11-01

We introduce the preference parameter into the two-route dynamic model proposed by Wahle et al. The parameter represents the driver’s preference for the route choice. When the driver prefers a route, the traffic flow on route A does not balance with that on route B. We study the signal control for the unbalanced two-route traffic flow at the tour-time feedback strategy where the vehicles move ahead through a series of signals. The traffic signals are controlled by both cycle time and phase shift (offset time). We find that the mean tour time can be balanced by selecting the offset time successfully. We derive the relationship between the mean tour time and offset time (phase shift). Also, the dependences of the mean density and mean current on the offset time are derived.

Supporting the Future Air Traffic Control Projection Process

NASA Technical Reports Server (NTRS)

Davison, Hayley J.; Hansman, R. John, Jr.

2002-01-01

In air traffic control, projecting what the air traffic situation will be over the next 30 seconds to 30 minutes is a key process in identifying conflicts that may arise so that evasive action can be taken upon discovery of these conflicts. A series of field visits in the Boston and New York terminal radar approach control (TRACON) facilities and in the oceanic air traffic control facilities in New York and Reykjavik, Iceland were conducted to investigate the projection process in two different ATC domains. The results from the site visits suggest that two types of projection are currently used in ATC tasks, depending on the type of separation minima and/or traffic restriction and information display used by the controller. As technologies improve and procedures change, care should be taken by designers to support projection through displays, automation, and procedures. It is critical to prevent time/space mismatches between interfaces and restrictions. Existing structure in traffic dynamics could be utilized to provide controllers with useful behavioral models on which to build projections. Subtle structure that the controllers are unable to internalize could be incorporated into an ATC projection aid.

Partial and Total Annoyance Due to Road Traffic Noise Combined with Aircraft or Railway Noise: Structural Equation Analysis.

PubMed

Gille, Laure-Anne; Marquis-Favre, Catherine; Lam, Kin-Che

2017-11-30

Structural equation modeling was used to analyze partial and total in situ annoyance in combined transportation noise situations. A psychophysical total annoyance model and a perceptual total annoyance model were proposed. Results show a high contribution of Noise exposure and Noise sensitivity to Noise annoyance , as well as a causal relationship between noise annoyance and lower Dwelling satisfaction. Moreover, the Visibility of noise source may increase noise annoyance, even when the visible noise source is different from the annoying source under study. With regards to total annoyance due to road traffic noise combined with railway or aircraft noise, even though in both situations road traffic noise may be considered background noise and the other noise source event noise, the contribution of road traffic noise to the models is greater than railway noise and smaller than aircraft noise. This finding may be explained by the difference in sound pressure levels between these two types of combined exposures or by the aircraft noise level, which may also indicate the city in which the respondents live. Finally, the results highlight the importance of sample size and variable distribution in the database, as different results can be observed depending on the sample or variables considered.

Partial and Total Annoyance Due to Road Traffic Noise Combined with Aircraft or Railway Noise: Structural Equation Analysis

PubMed Central

Gille, Laure-Anne; Marquis-Favre, Catherine; Lam, Kin-Che

2017-01-01

Structural equation modeling was used to analyze partial and total in situ annoyance in combined transportation noise situations. A psychophysical total annoyance model and a perceptual total annoyance model were proposed. Results show a high contribution of Noise exposure and Noise sensitivity to Noise annoyance, as well as a causal relationship between noise annoyance and lower Dwelling satisfaction. Moreover, the Visibility of noise source may increase noise annoyance, even when the visible noise source is different from the annoying source under study. With regards to total annoyance due to road traffic noise combined with railway or aircraft noise, even though in both situations road traffic noise may be considered background noise and the other noise source event noise, the contribution of road traffic noise to the models is greater than railway noise and smaller than aircraft noise. This finding may be explained by the difference in sound pressure levels between these two types of combined exposures or by the aircraft noise level, which may also indicate the city in which the respondents live. Finally, the results highlight the importance of sample size and variable distribution in the database, as different results can be observed depending on the sample or variables considered. PMID:29189751

Air Traffic Control Improvement Using Prioritized CSMA

NASA Technical Reports Server (NTRS)

Robinson, Daryl C.

2001-01-01

Version 7 simulations of the industry-standard network simulation software "OPNET" are presented of two applications of the Aeronautical Telecommunications Network (ATN), Controller Pilot Data Link Communications (CPDLC) and Automatic Dependent Surveillance-Broadcast mode (ADS-B), over VHF Data Link mode 2 (VDL-2). Communication is modeled for air traffic between just three cities. All aircraft are assumed to have the same equipage. The simulation involves Air Traffic Control (ATC) ground stations and 105 aircraft taking off, flying realistic free-flight trajectories, and landing in a 24-hr period. All communication is modeled as unreliable. Collision-less, prioritized carrier sense multiple access (CSMA) is successfully tested. The statistics presented include latency, queue length, and packet loss. This research may show that a communications system simpler than the currently accepted standard envisioned may not only suffice, but also surpass performance of the standard at a lower cost of deployment.

The use of random forests in modelling short-term air pollution effects based on traffic and meteorological conditions: A case study in Wrocław.

PubMed

Kamińska, Joanna A

2018-07-01

Random forests, an advanced data mining method, are used here to model the regression relationships between concentrations of the pollutants NO 2 , NO x and PM 2.5 , and nine variables describing meteorological conditions, temporal conditions and traffic flow. The study was based on hourly values of wind speed, wind direction, temperature, air pressure and relative humidity, temporal variables, and finally traffic flow, in the two years 2015 and 2016. An air quality measurement station was selected on a main road, located a short distance (40 m) from a large intersection equipped with a traffic flow measurement system. Nine different time subsets were defined, based among other things on the climatic conditions in Wrocław. An analysis was made of the fit of models created for those subsets, and of the importance of the predictors. Both the fit and the importance of particular predictors were found to be dependent on season. The best fit was obtained for models created for the six-month warm season (April-September) and for the summer season (June-August). The most important explanatory variable in the models of concentrations of nitrogen oxides was traffic flow, while in the case of PM 2.5 the most important were meteorological conditions, in particular temperature, wind speed and wind direction. Temporal variables (except for month in the case of PM 2.5 ) were found to have no significant effect on the concentrations of the studied pollutants. Copyright © 2018 Elsevier Ltd. All rights reserved.

Cognitive process modelling of controllers in en route air traffic control.

PubMed

Inoue, Satoru; Furuta, Kazuo; Nakata, Keiichi; Kanno, Taro; Aoyama, Hisae; Brown, Mark

2012-01-01

In recent years, various efforts have been made in air traffic control (ATC) to maintain traffic safety and efficiency in the face of increasing air traffic demands. ATC is a complex process that depends to a large degree on human capabilities, and so understanding how controllers carry out their tasks is an important issue in the design and development of ATC systems. In particular, the human factor is considered to be a serious problem in ATC safety and has been identified as a causal factor in both major and minor incidents. There is, therefore, a need to analyse the mechanisms by which errors occur due to complex factors and to develop systems that can deal with these errors. From the cognitive process perspective, it is essential that system developers have an understanding of the more complex working processes that involve the cooperative work of multiple controllers. Distributed cognition is a methodological framework for analysing cognitive processes that span multiple actors mediated by technology. In this research, we attempt to analyse and model interactions that take place in en route ATC systems based on distributed cognition. We examine the functional problems in an ATC system from a human factors perspective, and conclude by identifying certain measures by which to address these problems. This research focuses on the analysis of air traffic controllers' tasks for en route ATC and modelling controllers' cognitive processes. This research focuses on an experimental study to gain a better understanding of controllers' cognitive processes in air traffic control. We conducted ethnographic observations and then analysed the data to develop a model of controllers' cognitive process. This analysis revealed that strategic routines are applicable to decision making.

Droplet Traffic Control at a simple T junction

NASA Astrophysics Data System (ADS)

Panizza, Pascal; Engl, Wilfried; Colin, Annie; Ajdari, Armand

2006-03-01

A basic yet essential element of every traffic flow control is the effect of a junction where the flow is separated into several streams. How do pedestrians, vehicles or blood cells divide when they reach a junction? How does the outcome depend on their density? Similar fundamental questions hold for much simpler systems: in this paper, we have studied the behaviour of periodic trains of water droplets flowing in oil through a channel as they reach a simple, locally symmetric, T junction. Depending on their dilution, we observe that the droplets are either alternately partitioned between both outlets or sorted exclusively into the shortest one. We show that this surprising behaviour results from the hydrodynamic feed-back of drops in the two outlets on the selection process occurring at the junction. Our results offer a first guide for the design and modelling of droplet traffic in complex branched networks, a necessary step towards parallelized droplet-based ``lab-on-chip'' devices.

Bayes classifiers for imbalanced traffic accidents datasets.

PubMed

Mujalli, Randa Oqab; López, Griselda; Garach, Laura

2016-03-01

Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the slight injuries instances whereas the killed or severe injuries instances are misclassified frequently. Based on traffic accidents data collected on urban and suburban roads in Jordan for three years (2009-2011); three different data balancing techniques were used: under-sampling which removes some instances of the majority class, oversampling which creates new instances of the minority class and a mix technique that combines both. In addition, different Bayes classifiers were compared for the different imbalanced and balanced data sets: Averaged One-Dependence Estimators, Weightily Average One-Dependence Estimators, and Bayesian networks in order to identify factors that affect the severity of an accident. The results indicated that using the balanced data sets, especially those created using oversampling techniques, with Bayesian networks improved classifying a traffic accident according to its severity and reduced the misclassification of killed and severe injuries instances. On the other hand, the following variables were found to contribute to the occurrence of a killed causality or a severe injury in a traffic accident: number of vehicles involved, accident pattern, number of directions, accident type, lighting, surface condition, and speed limit. This work, to the knowledge of the authors, is the first that aims at analyzing historical data records for traffic accidents occurring in Jordan and the first to apply balancing techniques to analyze injury severity of traffic accidents. Copyright © 2015 Elsevier Ltd. All rights reserved.

Estimating traffic volumes for signalized intersections using connected vehicle data

DOE PAGES

Zheng, Jianfeng; Liu, Henry X.

2017-04-17

Recently connected vehicle (CV) technology has received significant attention thanks to active pilot deployments supported by the US Department of Transportation (USDOT). At signalized intersections, CVs may serve as mobile sensors, providing opportunities of reducing dependencies on conventional vehicle detectors for signal operation. However, most of the existing studies mainly focus on scenarios that penetration rates of CVs reach certain level, e.g., 25%, which may not be feasible in the near future. How to utilize data from a small number of CVs to improve traffic signal operation remains an open question. In this work, we develop an approach to estimatemore » traffic volume, a key input to many signal optimization algorithms, using GPS trajectory data from CV or navigation devices under low market penetration rates. To estimate traffic volumes, we model in this paper vehicle arrivals at signalized intersections as a time-dependent Poisson process, which can account for signal coordination. The estimation problem is formulated as a maximum likelihood problem given multiple observed trajectories from CVs approaching to the intersection. An expectation maximization (EM) procedure is derived to solve the estimation problem. Two case studies were conducted to validate our estimation algorithm. One uses the CV data from the Safety Pilot Model Deployment (SPMD) project, in which around 2800 CVs were deployed in the City of Ann Arbor, MI. The other uses vehicle trajectory data from users of a commercial navigation service in China. Mean absolute percentage error (MAPE) of the estimation is found to be 9–12%, based on benchmark data manually collected and data from loop detectors. Finally, considering the existing scale of CV deployments, the proposed approach could be of significant help to traffic management agencies for evaluating and operating traffic signals, paving the way of using CVs for detector-free signal operation in the future.« less

Estimating traffic volumes for signalized intersections using connected vehicle data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zheng, Jianfeng; Liu, Henry X.

Recently connected vehicle (CV) technology has received significant attention thanks to active pilot deployments supported by the US Department of Transportation (USDOT). At signalized intersections, CVs may serve as mobile sensors, providing opportunities of reducing dependencies on conventional vehicle detectors for signal operation. However, most of the existing studies mainly focus on scenarios that penetration rates of CVs reach certain level, e.g., 25%, which may not be feasible in the near future. How to utilize data from a small number of CVs to improve traffic signal operation remains an open question. In this work, we develop an approach to estimatemore » traffic volume, a key input to many signal optimization algorithms, using GPS trajectory data from CV or navigation devices under low market penetration rates. To estimate traffic volumes, we model in this paper vehicle arrivals at signalized intersections as a time-dependent Poisson process, which can account for signal coordination. The estimation problem is formulated as a maximum likelihood problem given multiple observed trajectories from CVs approaching to the intersection. An expectation maximization (EM) procedure is derived to solve the estimation problem. Two case studies were conducted to validate our estimation algorithm. One uses the CV data from the Safety Pilot Model Deployment (SPMD) project, in which around 2800 CVs were deployed in the City of Ann Arbor, MI. The other uses vehicle trajectory data from users of a commercial navigation service in China. Mean absolute percentage error (MAPE) of the estimation is found to be 9–12%, based on benchmark data manually collected and data from loop detectors. Finally, considering the existing scale of CV deployments, the proposed approach could be of significant help to traffic management agencies for evaluating and operating traffic signals, paving the way of using CVs for detector-free signal operation in the future.« less

ADS-B within a Multi-Aircraft Simulation for Distributed Air-Ground Traffic Management

NASA Technical Reports Server (NTRS)

Barhydt, Richard; Palmer, Michael T.; Chung, William W.; Loveness, Ghyrn W.

2004-01-01

Automatic Dependent Surveillance Broadcast (ADS-B) is an enabling technology for NASA s Distributed Air-Ground Traffic Management (DAG-TM) concept. DAG-TM has the goal of significantly increasing capacity within the National Airspace System, while maintaining or improving safety. Under DAG-TM, aircraft exchange state and intent information over ADS-B with other aircraft and ground stations. This information supports various surveillance functions including conflict detection and resolution, scheduling, and conformance monitoring. To conduct more rigorous concept feasibility studies, NASA Langley Research Center s PC-based Air Traffic Operations Simulation models a 1090 MHz ADS-B communication structure, based on industry standards for message content, range, and reception probability. The current ADS-B model reflects a mature operating environment and message interference effects are limited to Mode S transponder replies and ADS-B squitters. This model was recently evaluated in a Joint DAG-TM Air/Ground Coordination Experiment with NASA Ames Research Center. Message probability of reception vs. range was lower at higher traffic levels. The highest message collision probability occurred near the meter fix serving as the confluence for two arrival streams. Even the highest traffic level encountered in the experiment was significantly less than the industry standard "LA Basin 2020" scenario. Future studies will account for Mode A and C message interference (a major effect in several industry studies) and will include Mode A and C aircraft in the simulation, thereby increasing the total traffic level. These changes will support ongoing enhancements to separation assurance functions that focus on accommodating longer ADS-B information update intervals.

Large-scale measurement and modeling of backbone Internet traffic

NASA Astrophysics Data System (ADS)

Roughan, Matthew; Gottlieb, Joel

2002-07-01

There is a brewing controversy in the traffic modeling community concerning how to model backbone traffic. The fundamental work on self-similarity in data traffic appears to be contradicted by recent findings that suggest that backbone traffic is smooth. The traffic analysis work to date has focused on high-quality but limited-scope packet trace measurements; this limits its applicability to high-speed backbone traffic. This paper uses more than one year's worth of SNMP traffic data covering an entire Tier 1 ISP backbone to address the question of how backbone network traffic should be modeled. Although the limitations of SNMP measurements do not permit us to comment on the fine timescale behavior of the traffic, careful analysis of the data suggests that irrespective of the variation at fine timescales, we can construct a simple traffic model that captures key features of the observed traffic. Furthermore, the model's parameters are measurable using existing network infrastructure, making this model practical in a present-day operational network. In addition to its practicality, the model verifies basic statistical multiplexing results, and thus sheds deep insight into how smooth backbone traffic really is.

Dividing traffic cluster into parts by signal control

NASA Astrophysics Data System (ADS)

Nagatani, Takashi

2018-02-01

When a cluster of vehicles with various speeds moves through the series of signals, the cluster breaks down by stopping at signals and results in smaller groups of vehicles. We present the nonlinear-map model of the motion of vehicles controlled by the signals. We study the breakup of a cluster of vehicles through the series of signals. The cluster of vehicles is divided into various groups by controlling the cycle time of signals. The vehicles within each group move with the same mean velocity. The breakup of the traffic cluster depends highly on the signal control. The dependence of dividing on both cycle time and vehicular speed is clarified. Also, we investigate the effect of the irregular interval between signals on dividing.

Automatic Dependent Surveillance Broadcast (ADS-B) System for Ownership and Traffic Situational Awareness

NASA Technical Reports Server (NTRS)

Arteaga, Ricardo A. (Inventor)

2016-01-01

The present invention proposes an automatic dependent surveillance broadcast (ADS-B) architecture and process, in which priority aircraft and ADS-B IN traffic information are included in the transmission of data through the telemetry communications to a remote ground control station. The present invention further proposes methods for displaying general aviation traffic information in three and/or four dimension trajectories using an industry standard Earth browser for increased situation awareness and enhanced visual acquisition of traffic for conflict detection. The present invention enable the applications of enhanced visual acquisition of traffic, traffic alerts, and en-route and terminal surveillance used to augment pilot situational awareness through ADS-B IN display and information in three or four dimensions for self-separation awareness.

Role of highway traffic on spatial and temporal distributions of air pollutants in a Swiss Alpine valley.

PubMed

Ducret-Stich, Regina E; Tsai, Ming-Yi; Ragettli, Martina S; Ineichen, Alex; Kuenzli, Nino; Phuleria, Harish C

2013-07-01

Traffic-related air pollutants show high spatial variability near roads, posing a challenge to adequately assess exposures. Recent modeling approaches (e.g. dispersion models, land-use regression (LUR) models) have addressed this but mostly in urban areas where traffic is abundant. In contrast, our study area was located in a rural Swiss Alpine valley crossed by the main North-south transit highway of Switzerland. We conducted an extensive measurement campaign collecting continuous nitrogen dioxide (NO₂), particulate number concentrations (PN), daily respirable particulate matter (PM10), elemental carbon (EC) and organic carbon (OC) at one background, one highway and seven mobile stations from November 2007 to June 2009. Using these measurements, we built a hybrid model to predict daily outdoor NO₂ concentrations at residences of children participating in an asthma panel study. With the exception of OC, daily variations of the pollutants followed the temporal trends of heavy-duty traffic counts on the highway. In contrast, variations of weekly/seasonal means were strongly determined by meteorological conditions, e.g., winter inversion episodes. For pollutants related to primary exhaust emissions (i.e. NO₂, EC and PN) local spatial variation strongly depended on proximity to the highway. Pollutant concentrations decayed to background levels within 150 to 200 m from the highway. Two separate daily NO₂ prediction models were built using LUR approaches with (a) short-term traffic and weather data (model 1) and (b) subsequent addition of daily background NO₂ to previous model (model 2). Models 1 and 2 explained 70% and 91% of the variability in outdoor NO₂ concentrations, respectively. The biweekly averaged predictions from the final model 2 agreed very well with the independent biweekly integrated passive measurements taken at thirteen homes and nine community sites (validation R(2)=0.74). The excellent spatio-temporal performance of our model provides a very promising basis for the health effect assessment of the panel study. Copyright © 2013 Elsevier B.V. All rights reserved.

Methods to improve traffic flow and noise exposure estimation on minor roads.

PubMed

Morley, David W; Gulliver, John

2016-09-01

Address-level estimates of exposure to road traffic noise for epidemiological studies are dependent on obtaining data on annual average daily traffic (AADT) flows that is both accurate and with good geographical coverage. National agencies often have reliable traffic count data for major roads, but for residential areas served by minor roads, especially at national scale, such information is often not available or incomplete. Here we present a method to predict AADT at the national scale for minor roads, using a routing algorithm within a geographical information system (GIS) to rank roads by importance based on simulated journeys through the road network. From a training set of known minor road AADT, routing importance is used to predict AADT on all UK minor roads in a regression model along with the road class, urban or rural location and AADT on the nearest major road. Validation with both independent traffic counts and noise measurements show that this method gives a considerable improvement in noise prediction capability when compared to models that do not give adequate consideration to minor road variability (Spearman's rho. increases from 0.46 to 0.72). This has significance for epidemiological cohort studies attempting to link noise exposure to adverse health outcomes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Transport Infrastructure Shapes Foraging Habitat in a Raptor Community

PubMed Central

Planillo, Aimara; Kramer-Schadt, Stephanie; Malo, Juan E.

2015-01-01

Transport infrastructure elements are widespread and increasing in size and length in many countries, with the subsequent alteration of landscapes and wildlife communities. Nonetheless, their effects on habitat selection by raptors are still poorly understood. In this paper, we analyzed raptors’ foraging habitat selection in response to conventional roads and high capacity motorways at the landscape scale, and compared their effects with those of other variables, such as habitat structure, food availability, and presence of potential interspecific competitors. We also analyzed whether the raptors’ response towards infrastructure depends on the spatial scale of observation, comparing the attraction or avoidance behavior of the species at the landscape scale with the response of individuals observed in the proximity of the infrastructure. Based on ecological hypotheses for foraging habitat selection, we built generalized linear mixed models, selected the best models according to Akaike Information Criterion and assessed variable importance by Akaike weights. At the community level, the traffic volume was the most relevant variable in the landscape for foraging habitat selection. Abundance, richness, and diversity values reached their maximum at medium traffic volumes and decreased at highest traffic volumes. Individual species showed different degrees of tolerance toward traffic, from higher abundance in areas with high traffic values to avoidance of it. Medium-sized opportunistic raptors increased their abundance near the traffic infrastructures, large scavenger raptors avoided areas with higher traffic values, and other species showed no direct response to traffic but to the presence of prey. Finally, our cross-scale analysis revealed that the effect of transport infrastructures on the behavior of some species might be detectable only at a broad scale. Also, food availability may attract raptor species to risky areas such as motorways. PMID:25786218

Transport infrastructure shapes foraging habitat in a raptor community.

PubMed

Planillo, Aimara; Kramer-Schadt, Stephanie; Malo, Juan E

2015-01-01

Transport infrastructure elements are widespread and increasing in size and length in many countries, with the subsequent alteration of landscapes and wildlife communities. Nonetheless, their effects on habitat selection by raptors are still poorly understood. In this paper, we analyzed raptors' foraging habitat selection in response to conventional roads and high capacity motorways at the landscape scale, and compared their effects with those of other variables, such as habitat structure, food availability, and presence of potential interspecific competitors. We also analyzed whether the raptors' response towards infrastructure depends on the spatial scale of observation, comparing the attraction or avoidance behavior of the species at the landscape scale with the response of individuals observed in the proximity of the infrastructure. Based on ecological hypotheses for foraging habitat selection, we built generalized linear mixed models, selected the best models according to Akaike Information Criterion and assessed variable importance by Akaike weights. At the community level, the traffic volume was the most relevant variable in the landscape for foraging habitat selection. Abundance, richness, and diversity values reached their maximum at medium traffic volumes and decreased at highest traffic volumes. Individual species showed different degrees of tolerance toward traffic, from higher abundance in areas with high traffic values to avoidance of it. Medium-sized opportunistic raptors increased their abundance near the traffic infrastructures, large scavenger raptors avoided areas with higher traffic values, and other species showed no direct response to traffic but to the presence of prey. Finally, our cross-scale analysis revealed that the effect of transport infrastructures on the behavior of some species might be detectable only at a broad scale. Also, food availability may attract raptor species to risky areas such as motorways.

Statistical dependency in visual scanning

NASA Technical Reports Server (NTRS)

Ellis, Stephen R.; Stark, Lawrence

1986-01-01

A method to identify statistical dependencies in the positions of eye fixations is developed and applied to eye movement data from subjects who viewed dynamic displays of air traffic and judged future relative position of aircraft. Analysis of approximately 23,000 fixations on points of interest on the display identified statistical dependencies in scanning that were independent of the physical placement of the points of interest. Identification of these dependencies is inconsistent with random-sampling-based theories used to model visual search and information seeking.

Does Temperature Modify the Effects of Rain and Snow Precipitation on Road Traffic Injuries?

PubMed

Lee, Won-Kyung; Lee, Hye-Ah; Hwang, Seung-sik; Kim, Ho; Lim, Youn-Hee; Hong, Yun-Chul; Ha, Eun-Hee; Park, Hyesook

2015-01-01

There are few data on the interaction between temperature and snow and rain precipitation, although they could interact in their effects on road traffic injuries. The integrated database of the Korea Road Traffic Authority was used to calculate the daily frequency of road traffic injuries in Seoul. Weather data included rain and snow precipitation, temperature, pressure, and fog from May 2007 to December 2011. Precipitation of rain and snow were divided into nine and six temperature range categories, respectively. The interactive effects of temperature and rain and snow precipitation on road traffic injuries were analyzed using a generalized additive model with a Poisson distribution. The risk of road traffic injuries during snow increased when the temperature was below freezing. Road traffic injuries increased by 6.6% when it was snowing and above 0 °C, whereas they increased by 15% when it was snowing and at or below 0 °C. In terms of heavy rain precipitation, moderate temperatures were related to an increased prevalence of injuries. When the temperature was 0-20 °C, we found a 12% increase in road traffic injuries, whereas it increased by 8.5% and 6.8% when it was <0 °C and >20 °C, respectively. The interactive effect was consistent across the traffic accident subtypes. The effect of adverse weather conditions on road traffic injuries differed depending on the temperature. More road traffic injuries were related to rain precipitation when the temperature was moderate and to snow when it was below freezing.

Does Temperature Modify the Effects of Rain and Snow Precipitation on Road Traffic Injuries?

PubMed Central

Lee, Won-Kyung; Lee, Hye-Ah; Hwang, Seung-sik; Kim, Ho; Lim, Youn-Hee; Hong, Yun-Chul; Ha, Eun-Hee; Park, Hyesook

2015-01-01

Background There are few data on the interaction between temperature and snow and rain precipitation, although they could interact in their effects on road traffic injuries. Methods The integrated database of the Korea Road Traffic Authority was used to calculate the daily frequency of road traffic injuries in Seoul. Weather data included rain and snow precipitation, temperature, pressure, and fog from May 2007 to December 2011. Precipitation of rain and snow were divided into nine and six temperature range categories, respectively. The interactive effects of temperature and rain and snow precipitation on road traffic injuries were analyzed using a generalized additive model with a Poisson distribution. Results The risk of road traffic injuries during snow increased when the temperature was below freezing. Road traffic injuries increased by 6.6% when it was snowing and above 0°C, whereas they increased by 15% when it was snowing and at or below 0°C. In terms of heavy rain precipitation, moderate temperatures were related to an increased prevalence of injuries. When the temperature was 0–20°C, we found a 12% increase in road traffic injuries, whereas it increased by 8.5% and 6.8% when it was <0°C and >20°C, respectively. The interactive effect was consistent across the traffic accident subtypes. Conclusions The effect of adverse weather conditions on road traffic injuries differed depending on the temperature. More road traffic injuries were related to rain precipitation when the temperature was moderate and to snow when it was below freezing. PMID:26073021

Drinking, driving, and crashing: a traffic-flow model of alcohol-related motor vehicle accidents.

PubMed

Gruenewald, Paul J; Johnson, Fred W

2010-03-01

This study examined the influence of on-premise alcohol-outlet densities and of drinking-driver densities on rates of alcohol-related motor vehicle crashes. A traffic-flow model is developed to represent geographic relationships between residential locations of drinking drivers, alcohol outlets, and alcohol-related motor vehicle crashes. Cross-sectional and time-series cross-sectional spatial analyses were performed using data collected from 144 geographic units over 4 years. Data were obtained from archival and survey sources in six communities. Archival data were obtained within community areas and measured activities of either the resident population or persons visiting these communities. These data included local and highway traffic flow, locations of alcohol outlets, population density, network density of the local roadway system, and single-vehicle nighttime (SVN) crashes. Telephone-survey data obtained from residents of the communities were used to estimate the size of the resident drinking and driving population. Cross-sectional analyses showed that effects relating on-premise densities to alcohol-related crashes were moderated by highway trafficflow. Depending on levels of highway traffic flow, 10% greater densities were related to 0% to 150% greater rates of SVN crashes. Time-series cross-sectional analyses showed that changes in the population pool of drinking drivers and on-premise densities interacted to increase SVN crash rates. A simple traffic-flow model can assess the effects of on-premise alcohol-outlet densities and of drinking-driver densities as they vary across communities to produce alcohol-related crashes. Analyses based on these models can usefully guide policy decisions on the sitting of on-premise alcohol outlets.

From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks

PubMed Central

Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jianming

2016-01-01

The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains. PMID:26972968

From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks.

PubMed

Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jianming

2016-03-14

The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains.

Development of a traffic noise prediction model for an urban environment.

PubMed

Sharma, Asheesh; Bodhe, G L; Schimak, G

2014-01-01

The objective of this study is to develop a traffic noise model under diverse traffic conditions in metropolitan cities. The model has been developed to calculate equivalent traffic noise based on four input variables i.e. equivalent traffic flow (Q e ), equivalent vehicle speed (S e ) and distance (d) and honking (h). The traffic data is collected and statistically analyzed in three different cases for 15-min during morning and evening rush hours. Case I represents congested traffic where equivalent vehicle speed is <30 km/h while case II represents free-flowing traffic where equivalent vehicle speed is >30 km/h and case III represents calm traffic where no honking is recorded. The noise model showed better results than earlier developed noise model for Indian traffic conditions. A comparative assessment between present and earlier developed noise model has also been presented in the study. The model is validated with measured noise levels and the correlation coefficients between measured and predicted noise levels were found to be 0.75, 0.83 and 0.86 for case I, II and III respectively. The noise model performs reasonably well under different traffic conditions and could be implemented for traffic noise prediction at other region as well.

Development of a real-time crash risk prediction model incorporating the various crash mechanisms across different traffic states.

PubMed

Xu, Chengcheng; Wang, Wei; Liu, Pan; Zhang, Fangwei

2015-01-01

This study aimed to identify the traffic flow variables contributing to crash risks under different traffic states and to develop a real-time crash risk model incorporating the varying crash mechanisms across different traffic states. The crash, traffic, and geometric data were collected on the I-880N freeway in California in 2008 and 2009. This study considered 4 different traffic states in Wu's 4-phase traffic theory. They are free fluid traffic, bunched fluid traffic, bunched congested traffic, and standing congested traffic. Several different statistical methods were used to accomplish the research objective. The preliminary analysis showed that traffic states significantly affected crash likelihood, collision type, and injury severity. Nonlinear canonical correlation analysis (NLCCA) was conducted to identify the underlying phenomena that made certain traffic states more hazardous than others. The results suggested that different traffic states were associated with various collision types and injury severities. The matching of traffic flow characteristics and crash characteristics in NLCCA revealed how traffic states affected traffic safety. The logistic regression analyses showed that the factors contributing to crash risks were quite different across various traffic states. To incorporate the varying crash mechanisms across different traffic states, random parameters logistic regression was used to develop a real-time crash risk model. Bayesian inference based on Markov chain Monte Carlo simulations was used for model estimation. The parameters of traffic flow variables in the model were allowed to vary across different traffic states. Compared with the standard logistic regression model, the proposed model significantly improved the goodness-of-fit and predictive performance. These results can promote a better understanding of the relationship between traffic flow characteristics and crash risks, which is valuable knowledge in the pursuit of improving traffic safety on freeways through the use of dynamic safety management systems.

Investigation of Roadway Geometric and Traffic Flow Factors for Vehicle Crashes Using Spatiotemporal Interaction

NASA Astrophysics Data System (ADS)

Gill, G.; Sakrani, T.; Cheng, W.; Zhou, J.

2017-09-01

Traffic safety is a major concern in the transportation industry due to immense monetary and emotional burden caused by crashes of various severity levels, especially the injury and fatality ones. To reduce such crashes on all public roads, the safety management processes are commonly implemented which include network screening, problem diagnosis, countermeasure identification, and project prioritization. The selection of countermeasures for potential mitigation of crashes is governed by the influential factors which impact roadway crashes. Crash prediction model is the tool widely adopted by safety practitioners or researchers to link various influential factors to crash occurrences. Many different approaches have been used in the past studies to develop better fitting models which also exhibit prediction accuracy. In this study, a crash prediction model is developed to investigate the vehicular crashes occurring at roadway segments. The spatial and temporal nature of crash data is exploited to form a spatiotemporal model which accounts for the different types of heterogeneities among crash data and geometric or traffic flow variables. This study utilizes the Poisson lognormal model with random effects, which can accommodate the yearly variations in explanatory variables and the spatial correlations among segments. The dependency of different factors linked with roadway geometric, traffic flow, and road surface type on vehicular crashes occurring at segments was established as the width of lanes, posted speed limit, nature of pavement, and AADT were found to be correlated with vehicle crashes.

Spatial correlation analysis of urban traffic state under a perspective of community detection

NASA Astrophysics Data System (ADS)

Yang, Yanfang; Cao, Jiandong; Qin, Yong; Jia, Limin; Dong, Honghui; Zhang, Aomuhan

2018-05-01

Understanding the spatial correlation of urban traffic state is essential for identifying the evolution patterns of urban traffic state. However, the distribution of traffic state always has characteristics of large spatial span and heterogeneity. This paper adapts the concept of community detection to the correlation network of urban traffic state and proposes a new perspective to identify the spatial correlation patterns of traffic state. In the proposed urban traffic network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding correlation of traffic state. Further, the process of community detection in the urban traffic network (named GWPA-K-means) is applied to analyze the spatial dependency of traffic state. The proposed method extends the traditional K-means algorithm in two steps: (i) redefines the initial cluster centers by two properties of nodes (the GWPA value and the minimum shortest path length); (ii) utilizes the weight signal propagation process to transfer the topological information of the urban traffic network into a node similarity matrix. Finally, numerical experiments are conducted on a simple network and a real urban road network in Beijing. The results show that GWPA-K-means algorithm is valid in spatial correlation analysis of traffic state. The network science and community structure analysis perform well in describing the spatial heterogeneity of traffic state on a large spatial scale.

Criticism of generally accepted fundamentals and methodologies of traffic and transportation theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kerner, Boris S.

It is explained why the set of the fundamental empirical features of traffic breakdown (a transition from free flow to congested traffic) should be the empirical basis for any traffic and transportation theory that can be reliable used for control and optimization in traffic networks. It is shown that generally accepted fundamentals and methodologies of traffic and transportation theory are not consistent with the set of the fundamental empirical features of traffic breakdown at a highway bottleneck. To these fundamentals and methodologies of traffic and transportation theory belong (i) Lighthill-Whitham-Richards (LWR) theory, (ii) the General Motors (GM) model class (formore » example, Herman, Gazis et al. GM model, Gipps’s model, Payne’s model, Newell’s optimal velocity (OV) model, Wiedemann’s model, Bando et al. OV model, Treiber’s IDM, Krauß’s model), (iii) the understanding of highway capacity as a particular stochastic value, and (iv) principles for traffic and transportation network optimization and control (for example, Wardrop’s user equilibrium (UE) and system optimum (SO) principles). Alternatively to these generally accepted fundamentals and methodologies of traffic and transportation theory, we discuss three-phase traffic theory as the basis for traffic flow modeling as well as briefly consider the network breakdown minimization (BM) principle for the optimization of traffic and transportation networks with road bottlenecks.« less

Model of cyclist accident characteristics in the city of Malang and Blitar

NASA Astrophysics Data System (ADS)

Arifin, M. Z.; Agustin, I. W.

2018-01-01

Utilization of bicycles as an environmentally friendly mode of transportation is reconcerned as the development of sustainable transportation programs. The use of bicycles in some developed countries such as the Netherlands is 27 per cent of total travel, while for developing countries such as Indonesia, cyclists are less than 1% of total travel with low educated characteristics (65 per cent) and low income (48 per cent). Cyclist reduces dependencies on petroleum and environmental pollution as well as lessen the occurrence of congestion and traffic accidents involving motor vehicles. It was necessary to know the behavior and interaction of bicycle riders with other vehicle users in a heterogeneous traffic flow. The main purpose of the research is to create a model of bicycle accidents to increase the road traffic safety in Malang city and Blitar city. The research used analyses of frequency, the road’s level of service, and multiple linear regression. The results showed that there was a need for a development basis of a special lane for bicycle. It aims to reduce the level and number of cyclist accidents and to achieve transportation safety as well as to raise public awareness in traffic safety.

A Sarsa(λ)-based control model for real-time traffic light coordination.

PubMed

Zhou, Xiaoke; Zhu, Fei; Liu, Quan; Fu, Yuchen; Huang, Wei

2014-01-01

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.

[Clinical resulting risks in the persons injured in the traffic accident depending on the role and position in the traffic].

PubMed

Hur'iev, S O; Chundak, S S; Satsyk, S P

2013-03-01

Clinical resulting risks of the abdominal organs trauma in the injured persons, as a consequence of a traffic accident, constitute an important component of multicomponent polysystem trauma for the sign of taking part in a traffic.

Collective Traffic-like Movement of Ants on a Trail: Dynamical Phases and Phase Transitions

NASA Astrophysics Data System (ADS)

Kunwar, Ambarish; John, Alexander; Nishinari, Katsuhiro; Schadschneider, Andreas; Chowdhury, Debashish

2004-11-01

The traffic-like collective movement of ants on a trail can be described by a stochastic cellular automaton model. We have earlier investigated its unusual flow-density relation by using various mean field approximations and computer simulations. In this paper, we study the model following an alternative approach based on the analogy with the zero range process, which is one of the few known exactly solvable stochastic dynamical models. We show that our theory can quantitatively account for the unusual non-monotonic dependence of the average speed of the ants on their density for finite lattices with periodic boundary conditions. Moreover, we argue that the model exhibits a continuous phase transition at the critial density only in a limiting case. Furthermore, we investigate the phase diagram of the model by replacing the periodic boundary conditions by open boundary conditions.

Geosynchronous platform definition study. Volume 4, Part 1: Traffic analysis and system requirements for the baseline traffic model

NASA Technical Reports Server (NTRS)

1973-01-01

The traffic analyses and system requirements data generated in the study resulted in the development of two traffic models; the baseline traffic model and the new traffic model. The baseline traffic model provides traceability between the numbers and types of geosynchronous missions considered in the study and the entire spectrum of missions foreseen in the total national space program. The information presented pertaining to the baseline traffic model includes: (1) definition of the baseline traffic model, including identification of specific geosynchronous missions and their payload delivery schedules through 1990; (2) Satellite location criteria, including the resulting distribution of the satellite population; (3) Geosynchronous orbit saturation analyses, including the effects of satellite physical proximity and potential electromagnetic interference; and (4) Platform system requirements analyses, including satellite and mission equipment descriptions, the options and limitations in grouping satellites, and on-orbit servicing criteria (both remotely controlled and man-attended).

Hot emission model for mobile sources: application to the metropolitan region of the city of Santiago, Chile.

PubMed

Corvalán, Roberto M; Osses, Mauricio; Urrutia, Cristian M

2002-02-01

Depending on the final application, several methodologies for traffic emission estimation have been developed. Emission estimation based on total miles traveled or other average factors is a sufficient approach only for extended areas such as national or worldwide areas. For road emission control and strategies design, microscale analysis based on real-world emission estimations is often required. This involves actual driving behavior and emission factors of the local vehicle fleet under study. This paper reports on a microscale model for hot road emissions and its application to the metropolitan region of the city of Santiago, Chile. The methodology considers the street-by-street hot emission estimation with its temporal and spatial distribution. The input data come from experimental emission factors based on local driving patterns and traffic surveys of traffic flows for different vehicle categories. The methodology developed is able to estimate hourly hot road CO, total unburned hydrocarbons (THCs), particulate matter (PM), and NO(x) emissions for predefined day types and vehicle categories.

Automobile-dependency as a barrier to vision zero, evidence from the states in the USA.

PubMed

Ahangari, Hamed; Atkinson-Palombo, Carol; Garrick, Norman W

2017-10-01

With a traffic fatality rate of 10.6 per 100,000 as of 2013-more than triple that in the UK, the Netherlands, and Sweden-the United States has the worst traffic safety performance of all developed countries. Statewide variations are even more pronounced. North Dakota registers more than twice the national average and five times the rate of Massachusetts. We used panel models and annual data from 1997 to 2013 to capture the effect of seven separate sets of factors that influence traffic safety: exposure, travel behavior, socioeconomics, macroeconomics, safety policies, and mitigating factors such as health care. The results of our panel models and supplementary analysis of state effects show that two variables - Vehicle Miles Traveled and Vehicles per Capita-have the strongest impact on traffic fatality rates. This is closely followed by Infant Mortality Rates, the proxy that we used to represent the quality of health care. Policy levers such as Graduated Driver's Licenses (GDL) have improved safety, but to a limited extent. We also found that states with higher urban density and more walking are associated with lower traffic fatality rates. Taken as a whole, our findings suggest that if additional progress is to be made in reducing traffic fatalities, emphasis needs to move beyond simply focusing on policies such as GDL and seat belt laws, which have already been adopted by almost all jurisdictions across the United States. We need to also consider factors that focus on the type of urban form that we are creating to ensure that we are fostering environments that encourage multi-modal transportation such as walking to reduce the VMT and Vehicles per Capita, the two strongest predictors of traffic fatalities. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination

PubMed Central

Zhu, Fei; Liu, Quan; Fu, Yuchen; Huang, Wei

2014-01-01

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control. PMID:24592183

Removing Traffic Emissions from CO2 Time Series Measured at a Tall Tower Using on-Road Measurements and WRF-Stilt Transport Modeling

NASA Astrophysics Data System (ADS)

Schmidt, A.; Rella, C.; Goeckede, M.; Hanson, C. V.; Yang, Z.; Law, B. E.

2014-12-01

In recent years, measurements of atmospheric carbon dioxide with high precision and accuracy have become increasingly important for climate change research, in particular to inform terrestrial biosphere models. Anthropogenic carbon dioxide emissions from fossil fuel burning have long been recognized to contribute a significant portion of the carbon dioxide in the atmosphere. Here, we present an approach to remove the traffic related carbon dioxide emissions from mole fractions measured at a tall tower by using the corresponding carbon monoxide measurements in combination with footprint analyses and transport modeling. This technique improves the suitability of the CO2 data to be used in inverse modeling approaches of atmosphere-biosphere exchange that do not account for non-biotic portions of CO2. In our study region in Oregon, road traffic emissions are the biggest source of anthropogenic carbon dioxide and carbon monoxide. A three-day mobile campaign covering 1700 km of roads in northwestern Oregon was performed during summer of 2012 using a laser-based Cavity Ring Down Spectrometer. The mobile measurements incorporated different roads including main highways, urban streets, and back-roads, largely within the typical footprint of a tall CO2 observation tower in Oregon's Willamette Valley. For the first time, traffic related CO:CO2 emission ratios were measured directly at the sources during an on-road campaign under a variety of different driving conditions. An average emission ratio of 7.43 (±1.80) ppb CO per ppm CO2 was obtained for the study region and applied to separate the traffic related portion of CO2 from the mole fraction time series. The road traffic related portion of the CO2 mole fractions measured at the tower site reached maximum values from 9.8 to 12 ppm, depending on the height above the surface, during summer 2012.

Removing traffic emissions from CO2 time series measured at a tall tower using mobile measurements and transport modeling

NASA Astrophysics Data System (ADS)

Schmidt, Andres; Rella, Chris W.; Göckede, Mathias; Hanson, Chad; Yang, Zhenlin; Law, Beverly E.

2014-11-01

In recent years, measurements of atmospheric carbon dioxide with high precision and accuracy have become increasingly important for climate change research, in particular to inform terrestrial biosphere models. Anthropogenic carbon dioxide emissions from fossil fuel burning have long been recognized to contribute a significant portion of the carbon dioxide in the atmosphere. Here, we present an approach to remove the traffic related carbon dioxide emissions from mole fractions measured at a tall tower by using the corresponding carbon monoxide measurements in combination with footprint analyses and transport modeling. This technique improves the suitability of the CO2 data to be used in inverse modeling approaches of atmosphere-biosphere exchange that do not account for non-biotic portions of CO2. In our study region in Oregon, road traffic emissions are the biggest source of anthropogenic carbon dioxide and carbon monoxide. A three-day mobile campaign covering 1700 km of roads in northwestern Oregon was performed during summer of 2012 using a laser-based Cavity Ring-Down Spectrometer. The mobile measurements incorporated different roads including main highways, urban streets, and back-roads, largely within the typical footprint of a tall CO/CO2 observation tower in Oregon's Willamette Valley. For the first time, traffic related CO:CO2 emission ratios were measured directly at the sources during an on-road campaign under a variety of different driving conditions. An average emission ratio of 7.43 (±1.80) ppb CO per ppm CO2 was obtained for the study region and applied to separate the traffic related portion of CO2 from the mole fraction time series. The road traffic related portion of the CO2 mole fractions measured at the tower site reached maximum values ranging from 9.8 to 12 ppm, depending on the height above the surface, during summer 2012.

Multivariate dynamic Tobit models with lagged observed dependent variables: An effectiveness analysis of highway safety laws.

PubMed

Dong, Chunjiao; Xie, Kun; Zeng, Jin; Li, Xia

2018-04-01

Highway safety laws aim to influence driver behaviors so as to reduce the frequency and severity of crashes, and their outcomes. For one specific highway safety law, it would have different effects on the crashes across severities. Understanding such effects can help policy makers upgrade current laws and hence improve traffic safety. To investigate the effects of highway safety laws on crashes across severities, multivariate models are needed to account for the interdependency issues in crash counts across severities. Based on the characteristics of the dependent variables, multivariate dynamic Tobit (MVDT) models are proposed to analyze crash counts that are aggregated at the state level. Lagged observed dependent variables are incorporated into the MVDT models to account for potential temporal correlation issues in crash data. The state highway safety law related factors are used as the explanatory variables and socio-demographic and traffic factors are used as the control variables. Three models, a MVDT model with lagged observed dependent variables, a MVDT model with unobserved random variables, and a multivariate static Tobit (MVST) model are developed and compared. The results show that among the investigated models, the MVDT models with lagged observed dependent variables have the best goodness-of-fit. The findings indicate that, compared to the MVST, the MVDT models have better explanatory power and prediction accuracy. The MVDT model with lagged observed variables can better handle the stochasticity and dependency in the temporal evolution of the crash counts and the estimated values from the model are closer to the observed values. The results show that more lives could be saved if law enforcement agencies can make a sustained effort to educate the public about the importance of motorcyclists wearing helmets. Motor vehicle crash-related deaths, injuries, and property damages could be reduced if states enact laws for stricter text messaging rules, higher speeding fines, older licensing age, and stronger graduated licensing provisions. Injury and PDO crashes would be significantly reduced with stricter laws prohibiting the use of hand-held communication devices and higher fines for drunk driving. Copyright © 2018 Elsevier Ltd. All rights reserved.

Fast algorithm for radio propagation modeling in realistic 3-D urban environment

NASA Astrophysics Data System (ADS)

Rauch, A.; Lianghai, J.; Klein, A.; Schotten, H. D.

2015-11-01

Next generation wireless communication systems will consist of a large number of mobile or static terminals and should be able to fulfill multiple requirements depending on the current situation. Low latency and high packet success transmission rates should be mentioned in this context and can be summarized as ultra-reliable communications (URC). Especially for domains like mobile gaming, mobile video services but also for security relevant scenarios like traffic safety, traffic control systems and emergency management URC will be more and more required to guarantee a working communication between the terminals all the time.

Globally aligned states and hydrodynamic traffic jams in confined suspensions of active asymmetric particles.

PubMed

Lefauve, Adrien; Saintillan, David

2014-02-01

Strongly confined active liquids are subject to unique hydrodynamic interactions due to momentum screening and lubricated friction by the confining walls. Using numerical simulations, we demonstrate that two-dimensional dilute suspensions of fore-aft asymmetric polar swimmers in a Hele-Shaw geometry can exhibit a rich variety of novel phase behaviors depending on particle shape, including coherent polarized density waves with global alignment, persistent counterrotating vortices, density shocks and rarefaction waves. We also explain these phenomena using a linear stability analysis and a nonlinear traffic flow model, both derived from a mean-field kinetic theory.

Modeling particle number concentrations along Interstate 10 in El Paso, Texas

PubMed Central

Olvera, Hector A.; Jimenez, Omar; Provencio-Vasquez, Elias

2014-01-01

Annual average daily particle number concentrations around a highway were estimated with an atmospheric dispersion model and a land use regression model. The dispersion model was used to estimate particle concentrations along Interstate 10 at 98 locations within El Paso, Texas. This model employed annual averaged wind speed and annual average daily traffic counts as inputs. A land use regression model with vehicle kilometers traveled as the predictor variable was used to estimate local background concentrations away from the highway to adjust the near-highway concentration estimates. Estimated particle number concentrations ranged between 9.8 × 103 particles/cc and 1.3 × 105 particles/cc, and averaged 2.5 × 104 particles/cc (SE 421.0). Estimates were compared against values measured at seven sites located along I10 throughout the region. The average fractional error was 6% and ranged between -1% and -13% across sites. The largest bias of -13% was observed at a semi-rural site where traffic was lowest. The average bias amongst urban sites was 5%. The accuracy of the estimates depended primarily on the emission factor and the adjustment to local background conditions. An emission factor of 1.63 × 1014 particles/veh-km was based on a value proposed in the literature and adjusted with local measurements. The integration of the two modeling techniques ensured that the particle number concentrations estimates captured the impact of traffic along both the highway and arterial roadways. The performance and economical aspects of the two modeling techniques used in this study shows that producing particle concentration surfaces along major roadways would be feasible in urban regions where traffic and meteorological data are readily available. PMID:25313294

Development of bus-stop time models in dense urban areas : a case study in Washington DC.

DOT National Transportation Integrated Search

2015-08-01

Bus transit reliability depends on several factors including the route of travel, traffic conditions, time of day, and conditions at : the bus stops along the route. The number of passengers alighting or boarding, fare payment method, dwell time (DT)...

Importance of awareness in improving performance of emergency medical services (EMS) systems in enhancing traffic safety: A lesson from India.

PubMed

Vasudevan, Vinod; Singh, Preeti; Basu, Samyajit

2016-10-02

India has been slow in implementing a central emergency medical services (EMS) system across the country. "108 services" is one of the most popular services that is functional under the public-private partnership model. Limited available literature shows that despite access to services, many traffic crash victims are transported using private vehicles. The objective of this study is to understand the effectiveness of 108 services from a traffic safety perspective. A questionnaire survey is conducted to understand the awareness of EMS and their function. Using traffic-related fatalities as the dependent variable, a fixed effect panel data model is developed to analyze the effectiveness of the 108 services in improving the traffic safety. The results from the survey show that, in general, people are not aware of the 108 services. A majority of the population prefers taking victims to the hospital using their personal vehicles or any other vehicles available compared to calling an ambulance. Results from panel data analysis show that despite having an efficient system, these services failed to make significant improvement in the safety of road users in the states in which their services were subscribed. The lack of awareness of an important safety service is alarming. This could be a major reason for lower utilization of 108 services for transporting victims of traffic crashes. This article shows the importance of having efficient awareness campaigns to improve the efficiency of any similar programs that are aimed to enhance the safety of a region.

Delay-feedback control strategy for reducing CO2 emission of traffic flow system

NASA Astrophysics Data System (ADS)

Zhang, Li-Dong; Zhu, Wen-Xing

2015-06-01

To study the signal control strategy for reducing traffic emission theoretically, we first presented a kind of discrete traffic flow model with relative speed term based on traditional coupled map car-following model. In the model, the relative speed difference between two successive running cars is incorporated into following vehicle's acceleration running equation. Then we analyzed its stability condition with discrete control system stability theory. Third, we designed a delay-feedback controller to suppress traffic jam and decrease traffic emission based on modern controller theory. Last, numerical simulations are made to support our theoretical results, including the comparison of models' stability analysis, the influence of model type and signal control on CO2 emissions. The results show that the temporal behavior of our model is superior to other models, and the traffic signal controller has good effect on traffic jam suppression and traffic CO2 emission, which fully supports the theoretical conclusions.

Traffic flow behavior at un-signalized intersection with crossings pedestrians

NASA Astrophysics Data System (ADS)

Khallouk, A.; Echab, H.; Ez-Zahraouy, H.; Lakouari, N.

2018-02-01

Mixed traffic flux composed of crossing pedestrians and vehicles extensively exists in cities. To study the characteristics of the interference traffic flux, we develop a pedestrian-vehicle cellular automata model to present the interaction behaviors on a simple cross road. By realizing the fundamental parameters (i.e. injecting rates α1, α2, the extracting rate β and the pedestrian arrival rate αP), simulations are carried out. The vehicular traffic flux is calculated in terms of rates. The effect of the crosswalk can be regarded as a dynamic impurity. The system phase diagrams in the (α1 ,αP) plane are built. It is found that the phase diagrams consist essentially of four phases namely Free Flow, Congested, Maximal Current and Gridlock. The value of the Maximal current phase depends on the extracting rate β, while the Gridlock phase is achieved only when the pedestrians generating rate is higher than a critical value. Furthermore, the effect of vehicles changing lane (Pch1 ,Pch2) and the location of the crosswalk XP on the dynamic characteristics of vehicles flow are investigated. It is found that traffic situation in the system is slightly enhanced if the location of the crosswalks XP is far from the intersection. However, when Pch1, Pch2 increase, the traffic becomes congested and the Gridlock phase enlarges.

Traffic-aware energy saving scheme with modularization supporting in TWDM-PON

NASA Astrophysics Data System (ADS)

Xiong, Yu; Sun, Peng; Liu, Chuanbo; Guan, Jianjun

2017-01-01

Time and wavelength division multiplexed passive optical network (TWDM-PON) is considered to be a primary solution for next-generation passive optical network stage 2 (NG-PON2). Due to the feature of multi-wavelength transmission of TWDM-PON, some of the transmitters/receivers at the optical line terminal (OLT) could be shut down to reduce the energy consumption. Therefore, a novel scheme called traffic-aware energy saving scheme with modularization supporting is proposed. Through establishing the modular energy consumption model of OLT, the wavelength transmitters/receivers at OLT could be switched on or shut down adaptively depending on sensing the status of network traffic load, thus the energy consumption of OLT will be effectively reduced. Furthermore, exploring the technology of optical network unit (ONU) modularization, each module of ONU could be switched to sleep or active mode independently in order to reduce the energy consumption of ONU. Simultaneously, the polling sequence of ONU could be changed dynamically via sensing the packet arrival time. In order to guarantee the delay performance of network traffic, the sub-cycle division strategy is designed to transmit the real-time traffic preferentially. Finally, simulation results verify that the proposed scheme is able to reduce the energy consumption of the network while maintaining the traffic delay performance.

Laboratory Evaluation of Dynamic Traffic Assignment Systems: Requirements, Framework, and System Design

DOT National Transportation Integrated Search

1997-01-01

The success of Advanced Traveler Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS) depends on the availability and dissemination of timely and accurate estimates of current and emerging traffic network conditions. Real-time Dy...

Investigation of schedules for traffic signal timing optimization.

DOT National Transportation Integrated Search

2005-01-01

Traffic signal optimization is recognized as one of the most cost-effective ways to improve urban mobility; however the extent of the benefits realized could significantly depend on how often traffic signal re-optimization occurs. Using a case study ...

Symbols for cockpit displays of traffic information

DOT National Transportation Integrated Search

2009-10-25

A web-based study assessed pilots ability to learn and remember traffic symbols that may be shown on a Cockpit Display of Traffic Information (CDTI). These displays convey data obtained from Automatic Dependent Surveillance-Broadcast (ADS B) and rela...

Symbols for cockpit displays of traffic information

DOT National Transportation Integrated Search

2010-03-01

A web-based study assessed pilots ability to learn and remember traffic symbols that may be shown on a Cockpit Display of Traffic Information (CDTI). These displays convey data obtained from Automatic Dependent Surveillance-Broadcast (ADS B) and rela...

Comparing exposure assessment methods for traffic-related air pollution in an adverse pregnancy outcome study

PubMed Central

Wu, Jun; Wilhelm, Michelle; Chung, Judith; Ritz, Beate

2011-01-01

Background Previous studies reported adverse impacts of traffic-related air pollution exposure on pregnancy outcomes. Yet, little information exists on how effect estimates are impacted by the different exposure assessment methods employed in these studies. Objectives To compare effect estimates for traffic-related air pollution exposure and preeclampsia, preterm birth (gestational age less than 37 weeks), and very preterm birth (gestational age less than 30 weeks) based on four commonly-used exposure assessment methods. Methods We identified 81,186 singleton births during 1997–2006 at four hospitals in Los Angeles and Orange Counties, California. Exposures were assigned to individual subjects based on residential address at delivery using the nearest ambient monitoring station data [carbon monoxide (CO), nitrogen dioxide (NO2), nitric oxide (NO), nitrogen oxides (NOx), ozone (O3), and particulate matter less than 2.5 (PM2.5) or less than 10 (PM10) μm in aerodynamic diameter], both unadjusted and temporally-adjusted land-use regression (LUR) model estimates (NO, NO2, and NOx), CALINE4 line-source air dispersion model estimates (NOx and PM2.5), and a simple traffic-density measure. We employed unconditional logistic regression to analyze preeclampsia in our birth cohort, while for gestational age-matched risk sets with preterm and very preterm birth we employed conditional logistic regression. Results We observed elevated risks for preeclampsia, preterm birth, and very preterm birth from maternal exposures to traffic air pollutants measured at ambient stations (CO, NO, NO2, and NOx) and modeled through CALINE4 (NOx and PM2.5) and LUR (NO2 and NOx). Increased risk of preterm birth and very preterm birth were also positively associated with PM10 and PM2.5 air pollution measured at ambient stations. For LUR-modeled NO2 and NOx exposures, elevated risks for all the outcomes were observed in Los Angeles only – the region for which the LUR models were initially developed. Unadjusted LUR models often produced odds ratios somewhat larger in size than temporally-adjusted models. The size of effect estimates was smaller for exposures based on simpler traffic density measures than the other exposure assessment methods. Conclusion We generally confirmed that traffic-related air pollution was associated with adverse reproductive outcomes regardless of the exposure assessment method employed, yet the size of the estimated effect depended on how both temporal and spatial variations were incorporated into exposure assessment. The LUR model was not transferable even between two contiguous areas within the same large metropolitan area in Southern California. PMID:21453913

Methodologies for assessing the use-phase power consumption and greenhouse gas emissions of telecommunications network services.

PubMed

Chan, Chien A; Gygax, André F; Wong, Elaine; Leckie, Christopher A; Nirmalathas, Ampalavanapillai; Kilper, Daniel C

2013-01-02

Internet traffic has grown rapidly in recent years and is expected to continue to expand significantly over the next decade. Consequently, the resulting greenhouse gas (GHG) emissions of telecommunications service-supporting infrastructures have become an important issue. In this study, we develop a set of models for assessing the use-phase power consumption and carbon dioxide emissions of telecom network services to help telecom providers gain a better understanding of the GHG emissions associated with the energy required for their networks and services. Due to the fact that measuring the power consumption and traffic in a telecom network is a challenging task, these models utilize different granularities of available network information. As the granularity of the network measurement information decreases, the corresponding models have the potential to produce larger estimation errors. Therefore, we examine the accuracy of these models under various network scenarios using two approaches: (i) a sensitivity analysis through simulations and (ii) a case study of a deployed network. Both approaches show that the accuracy of the models depends on the network size, the total amount of network service traffic (i.e., for the service under assessment), and the number of network nodes used to process the service.

Analysis and Classification of Traffic in Wireless Sensor Network

DTIC Science & Technology

2007-03-01

34 1. Hurst Parameter ................................................................................35 2. Self-Similarity...traffic is self-similar, buffer size can be better designed from the forecasted traffic workload. 1. Hurst Parameter To determine the extent of self...similarity in WSN traffic, the Hurst parameter, H, is used. H also calculates the length of the long range dependence of a stochastic process. If H

Assessment of Traffic-Related Noise in Three Cities in the United States

PubMed Central

Lee, Eunice Y.; Jerrett, Michael; Ross, Zev; Coogan, Patricia F.; Seto, Edmund Y. W.

2014-01-01

Background Traffic-related noise is a growing public health concern in developing and developed countries due to increasing vehicle traffic. Epidemiological studies have reported associations between noise exposure and high blood pressure, increased risk of hypertension and heart disease, and stress induced by sleep disturbance and annoyance. These findings motivate the need for regular noise assessments within urban areas. This paper assesses the relationships between traffic and noise in three US cities. Methods Noise measurements were conducted in downtown areas in three cities in the United States: Atlanta, Los Angeles, and New York City. For each city, we measured ambient noise levels, and assessed their correlation with simultaneously measured vehicle counts, and with traffic data provided by local Metropolitan Planning Organizations (MPO). Additionally, measured noise levels were compared to noise levels predicted by the Federal Highway Administration’s Traffic Noise Model using (1) simultaneously measured traffic counts or (2) MPO traffic data sources as model input. Results We found substantial variations in traffic and noise within and between cities. Total number of vehicle counts explained a substantial amount of variation in measured ambient noise in Atlanta (78%), Los Angeles (58%), and New York City (62%). Modeled noise levels were moderately correlated with measured noise levels when observed traffic counts were used as model input. Weaker correlations were found when MPO traffic data was used as model input. Conclusions Ambient noise levels measured in all three cities were correlated with traffic data, highlighting the importance of traffic planning in mitigating noise-related health effects. Model performance was sensitive to the traffic data used as input. Future noise studies that use modeled noise estimates should evaluate traffic data quality and should ideally include other factors, such as local roadway, building, and meteorological characteristics. PMID:24792415

Traffic Circle Model

DOT National Transportation Integrated Search

1971-05-01

The report describes a dynamic model of a traffic circle which has been implemented on a CRT display terminal. The model includes sufficient parameters to allow changes in the structure of the traffic circle, the frequency of traffic introduced to th...

Control of Networked Traffic Flow Distribution - A Stochastic Distribution System Perspective

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Hong; Aziz, H M Abdul; Young, Stan

Networked traffic flow is a common scenario for urban transportation, where the distribution of vehicle queues either at controlled intersections or highway segments reflect the smoothness of the traffic flow in the network. At signalized intersections, the traffic queues are controlled by traffic signal control settings and effective traffic lights control would realize both smooth traffic flow and minimize fuel consumption. Funded by the Energy Efficient Mobility Systems (EEMS) program of the Vehicle Technologies Office of the US Department of Energy, we performed a preliminary investigation on the modelling and control framework in context of urban network of signalized intersections.more » In specific, we developed a recursive input-output traffic queueing models. The queue formation can be modeled as a stochastic process where the number of vehicles entering each intersection is a random number. Further, we proposed a preliminary B-Spline stochastic model for a one-way single-lane corridor traffic system based on theory of stochastic distribution control.. It has been shown that the developed stochastic model would provide the optimal probability density function (PDF) of the traffic queueing length as a dynamic function of the traffic signal setting parameters. Based upon such a stochastic distribution model, we have proposed a preliminary closed loop framework on stochastic distribution control for the traffic queueing system to make the traffic queueing length PDF follow a target PDF that potentially realizes the smooth traffic flow distribution in a concerned corridor.« less

Modeling the frequency of opposing left-turn conflicts at signalized intersections using generalized linear regression models.

PubMed

Zhang, Xin; Liu, Pan; Chen, Yuguang; Bai, Lu; Wang, Wei

2014-01-01

The primary objective of this study was to identify whether the frequency of traffic conflicts at signalized intersections can be modeled. The opposing left-turn conflicts were selected for the development of conflict predictive models. Using data collected at 30 approaches at 20 signalized intersections, the underlying distributions of the conflicts under different traffic conditions were examined. Different conflict-predictive models were developed to relate the frequency of opposing left-turn conflicts to various explanatory variables. The models considered include a linear regression model, a negative binomial model, and separate models developed for four traffic scenarios. The prediction performance of different models was compared. The frequency of traffic conflicts follows a negative binominal distribution. The linear regression model is not appropriate for the conflict frequency data. In addition, drivers behaved differently under different traffic conditions. Accordingly, the effects of conflicting traffic volumes on conflict frequency vary across different traffic conditions. The occurrences of traffic conflicts at signalized intersections can be modeled using generalized linear regression models. The use of conflict predictive models has potential to expand the uses of surrogate safety measures in safety estimation and evaluation.

Properties of Traffic Risk Coefficient

NASA Astrophysics Data System (ADS)

Tang, Tie-Qiao; Huang, Hai-Jun; Shang, Hua-Yan; Xue, Yu

2009-10-01

We use the model with the consideration of the traffic interruption probability (Physica A 387(2008)6845) to study the relationship between the traffic risk coefficient and the traffic interruption probability. The analytical and numerical results show that the traffic interruption probability will reduce the traffic risk coefficient and that the reduction is related to the density, which shows that this model can improve traffic security.

Traffic Games: Modeling Freeway Traffic with Game Theory

PubMed Central

Cortés-Berrueco, Luis E.; Gershenson, Carlos; Stephens, Christopher R.

2016-01-01

We apply game theory to a vehicular traffic model to study the effect of driver strategies on traffic flow. The resulting model inherits the realistic dynamics achieved by a two-lane traffic model and aims to incorporate phenomena caused by driver-driver interactions. To achieve this goal, a game-theoretic description of driver interaction was developed. This game-theoretic formalization allows one to model different lane-changing behaviors and to keep track of mobility performance. We simulate the evolution of cooperation, traffic flow, and mobility performance for different modeled behaviors. The analysis of these results indicates a mobility optimization process achieved by drivers’ interactions. PMID:27855176

Traffic Games: Modeling Freeway Traffic with Game Theory.

PubMed

Cortés-Berrueco, Luis E; Gershenson, Carlos; Stephens, Christopher R

2016-01-01

We apply game theory to a vehicular traffic model to study the effect of driver strategies on traffic flow. The resulting model inherits the realistic dynamics achieved by a two-lane traffic model and aims to incorporate phenomena caused by driver-driver interactions. To achieve this goal, a game-theoretic description of driver interaction was developed. This game-theoretic formalization allows one to model different lane-changing behaviors and to keep track of mobility performance. We simulate the evolution of cooperation, traffic flow, and mobility performance for different modeled behaviors. The analysis of these results indicates a mobility optimization process achieved by drivers' interactions.

Spatial and temporal associations of road traffic noise and air pollution in London: Implications for epidemiological studies.

PubMed

Fecht, Daniela; Hansell, Anna L; Morley, David; Dajnak, David; Vienneau, Danielle; Beevers, Sean; Toledano, Mireille B; Kelly, Frank J; Anderson, H Ross; Gulliver, John

2016-03-01

Road traffic gives rise to noise and air pollution exposures, both of which are associated with adverse health effects especially for cardiovascular disease, but mechanisms may differ. Understanding the variability in correlations between these pollutants is essential to understand better their separate and joint effects on human health. We explored associations between modelled noise and air pollutants using different spatial units and area characteristics in London in 2003-2010. We modelled annual average exposures to road traffic noise (LAeq,24h, Lden, LAeq,16h, Lnight) for ~190,000 postcode centroids in London using the UK Calculation of Road Traffic Noise (CRTN) method. We used a dispersion model (KCLurban) to model nitrogen dioxide, nitrogen oxide, ozone, total and the traffic-only component of particulate matter ≤2.5μm and ≤10μm. We analysed noise and air pollution correlations at the postcode level (~50 people), postcodes stratified by London Boroughs (~240,000 people), neighbourhoods (Lower layer Super Output Areas) (~1600 people), 1km grid squares, air pollution tertiles, 50m, 100m and 200m in distance from major roads and by deprivation tertiles. Across all London postcodes, we observed overall moderate correlations between modelled noise and air pollution that were stable over time (Spearman's rho range: |0.34-0.55|). Correlations, however, varied considerably depending on the spatial unit: largest ranges were seen in neighbourhoods and 1km grid squares (both Spearman's rho range: |0.01-0.87|) and was less for Boroughs (Spearman's rho range: |0.21-0.78|). There was little difference in correlations between exposure tertiles, distance from road or deprivation tertiles. Associations between noise and air pollution at the relevant geographical unit of analysis need to be carefully considered in any epidemiological analysis, in particular in complex urban areas. Low correlations near roads, however, suggest that independent effects of road noise and traffic-related air pollution can be reliably determined within London. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

An algorithm for generating data accessibility recommendations for flight deck Automatic Dependent Surveillance-Broadcast (ADS-B) applications

DOT National Transportation Integrated Search

2014-09-09

Automatic Dependent Surveillance-Broadcast (ADS-B) In technology supports the display of traffic data on Cockpit Displays of Traffic Information (CDTIs). The data are used by flightcrews to perform defined self-separation procedures, such as the in-t...

An emission processing system for air quality modelling in the Mexico City metropolitan area: Evaluation and comparison of the MOBILE6.2-Mexico and MOVES-Mexico traffic emissions.

PubMed

Guevara, M; Tena, C; Soret, A; Serradell, K; Guzmán, D; Retama, A; Camacho, P; Jaimes-Palomera, M; Mediavilla, A

2017-04-15

This article describes the High-Elective Resolution Modelling Emission System for Mexico (HERMES-Mex) model, an emission processing tool developed to transform the official Mexico City Metropolitan Area (MCMA) emission inventory into hourly, gridded (up to 1km 2 ) and speciated emissions used to drive mesoscale air quality simulations with the Community Multi-scale Air Quality (CMAQ) model. The methods and ancillary information used for the spatial and temporal disaggregation and speciation of the emissions are presented and discussed. The resulting emission system is evaluated, and a case study on CO, NO 2 , O 3 , VOC and PM 2.5 concentrations is conducted to demonstrate its applicability. Moreover, resulting traffic emissions from the Mobile Source Emission Factor Model for Mexico (MOBILE6.2-Mexico) and the MOtor Vehicle Emission Simulator for Mexico (MOVES-Mexico) models are integrated in the tool to assess and compare their performance. NO x and VOC total emissions modelled are reduced by 37% and 26% in the MCMA when replacing MOBILE6.2-Mexico for MOVES-Mexico traffic emissions. In terms of air quality, the system composed by the Weather Research and Forecasting model (WRF) coupled with the HERMES-Mex and CMAQ models properly reproduces the pollutant levels and patterns measured in the MCMA. The system's performance clearly improves in urban stations with a strong influence of traffic sources when applying MOVES-Mexico emissions. Despite reducing estimations of modelled precursor emissions, O 3 peak averages are increased in the MCMA core urban area (up to 30ppb) when using MOVES-Mexico mobile emissions due to its VOC-limited regime, while concentrations in the surrounding suburban/rural areas decrease or increase depending on the meteorological conditions of the day. The results obtained suggest that the HERMES-Mex model can be used to provide model-ready emissions for air quality modelling in the MCMA. Copyright © 2017 Elsevier B.V. All rights reserved.

Using floating car data to analyse the effects of its measures and eco-driving.

PubMed

Garcia-Castro, Alvaro; Monzon, Andres

2014-11-11

The road transportation sector is responsible for around 25% of total man-made CO2 emissions worldwide. Considerable efforts are therefore underway to reduce these emissions using several approaches, including improved vehicle technologies, traffic management and changing driving behaviour. Detailed traffic and emissions models are used extensively to assess the potential effects of these measures. However, if the input and calibration data are not sufficiently detailed there is an inherent risk that the results may be inaccurate. This article presents the use of Floating Car Data to derive useful speed and acceleration values in the process of traffic model calibration as a means of ensuring more accurate results when simulating the effects of particular measures. The data acquired includes instantaneous GPS coordinates to track and select the itineraries, and speed and engine performance extracted directly from the on-board diagnostics system. Once the data is processed, the variations in several calibration parameters can be analyzed by comparing the base case model with the measure application scenarios. Depending on the measure, the results show changes of up to 6.4% in maximum speed values, and reductions of nearly 15% in acceleration and braking levels, especially when eco-driving is applied.

Using Floating Car Data to Analyse the Effects of ITS Measures and Eco-Driving

PubMed Central

Garcia-Castro, Alvaro; Monzon, Andres

2014-01-01

The road transportation sector is responsible for around 25% of total man-made CO2 emissions worldwide. Considerable efforts are therefore underway to reduce these emissions using several approaches, including improved vehicle technologies, traffic management and changing driving behaviour. Detailed traffic and emissions models are used extensively to assess the potential effects of these measures. However, if the input and calibration data are not sufficiently detailed there is an inherent risk that the results may be inaccurate. This article presents the use of Floating Car Data to derive useful speed and acceleration values in the process of traffic model calibration as a means of ensuring more accurate results when simulating the effects of particular measures. The data acquired includes instantaneous GPS coordinates to track and select the itineraries, and speed and engine performance extracted directly from the on-board diagnostics system. Once the data is processed, the variations in several calibration parameters can be analyzed by comparing the base case model with the measure application scenarios. Depending on the measure, the results show changes of up to 6.4% in maximum speed values, and reductions of nearly 15% in acceleration and braking levels, especially when eco-driving is applied. PMID:25393787

Modeling the heterogeneous traffic correlations in urban road systems using traffic-enhanced community detection approach

NASA Astrophysics Data System (ADS)

Lu, Feng; Liu, Kang; Duan, Yingying; Cheng, Shifen; Du, Fei

2018-07-01

A better characterization of the traffic influence among urban roads is crucial for traffic control and traffic forecasting. The existence of spatial heterogeneity imposes great influence on modeling the extent and degree of road traffic correlation, which is usually neglected by the traditional distance based method. In this paper, we propose a traffic-enhanced community detection approach to spatially reveal the traffic correlation in city road networks. First, the road network is modeled as a traffic-enhanced dual graph with the closeness between two road segments determined not only by their topological connection, but also by the traffic correlation between them. Then a flow-based community detection algorithm called Infomap is utilized to identify the road segment clusters. Evaluated by Moran's I, Calinski-Harabaz Index and the traffic interpolation application, we find that compared to the distance based method and the community based method, our proposed traffic-enhanced community based method behaves better in capturing the extent of traffic relevance as both the topological structure of the road network and the traffic correlations among urban roads are considered. It can be used in more traffic-related applications, such as traffic forecasting, traffic control and guidance.

A new cellular automata model of traffic flow with negative exponential weighted look-ahead potential

NASA Astrophysics Data System (ADS)

Ma, Xiao; Zheng, Wei-Fan; Jiang, Bao-Shan; Zhang, Ji-Ye

2016-10-01

With the development of traffic systems, some issues such as traffic jams become more and more serious. Efficient traffic flow theory is needed to guide the overall controlling, organizing and management of traffic systems. On the basis of the cellular automata model and the traffic flow model with look-ahead potential, a new cellular automata traffic flow model with negative exponential weighted look-ahead potential is presented in this paper. By introducing the negative exponential weighting coefficient into the look-ahead potential and endowing the potential of vehicles closer to the driver with a greater coefficient, the modeling process is more suitable for the driver’s random decision-making process which is based on the traffic environment that the driver is facing. The fundamental diagrams for different weighting parameters are obtained by using numerical simulations which show that the negative exponential weighting coefficient has an obvious effect on high density traffic flux. The complex high density non-linear traffic behavior is also reproduced by numerical simulations. Project supported by the National Natural Science Foundation of China (Grant Nos. 11572264, 11172247, 11402214, and 61373009).

75 FR 48552 - Automatic Dependent Surveillance-Broadcast (ADS-B) Out Performance Requirements To Support Air...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-11

...-29305; Amdt. No. 91-314] RIN 2120-AI92 Automatic Dependent Surveillance-Broadcast (ADS-B) Out... Surveillance- Broadcast (ADS-B) Out Performance Requirements To Support Air Traffic Control (ATC) Service..., Surveillance and Broadcast Services, AJE-6, Air Traffic Organization, Federal Aviation Administration, 800...

Small-time Scale Network Traffic Prediction Based on Complex-valued Neural Network

NASA Astrophysics Data System (ADS)

Yang, Bin

2017-07-01

Accurate models play an important role in capturing the significant characteristics of the network traffic, analyzing the network dynamic, and improving the forecasting accuracy for system dynamics. In this study, complex-valued neural network (CVNN) model is proposed to further improve the accuracy of small-time scale network traffic forecasting. Artificial bee colony (ABC) algorithm is proposed to optimize the complex-valued and real-valued parameters of CVNN model. Small-scale traffic measurements data namely the TCP traffic data is used to test the performance of CVNN model. Experimental results reveal that CVNN model forecasts the small-time scale network traffic measurement data very accurately

Analysis of crash proportion by vehicle type at traffic analysis zone level: A mixed fractional split multinomial logit modeling approach with spatial effects.

PubMed

Lee, Jaeyoung; Yasmin, Shamsunnahar; Eluru, Naveen; Abdel-Aty, Mohamed; Cai, Qing

2018-02-01

In traffic safety literature, crash frequency variables are analyzed using univariate count models or multivariate count models. In this study, we propose an alternative approach to modeling multiple crash frequency dependent variables. Instead of modeling the frequency of crashes we propose to analyze the proportion of crashes by vehicle type. A flexible mixed multinomial logit fractional split model is employed for analyzing the proportions of crashes by vehicle type at the macro-level. In this model, the proportion allocated to an alternative is probabilistically determined based on the alternative propensity as well as the propensity of all other alternatives. Thus, exogenous variables directly affect all alternatives. The approach is well suited to accommodate for large number of alternatives without a sizable increase in computational burden. The model was estimated using crash data at Traffic Analysis Zone (TAZ) level from Florida. The modeling results clearly illustrate the applicability of the proposed framework for crash proportion analysis. Further, the Excess Predicted Proportion (EPP)-a screening performance measure analogous to Highway Safety Manual (HSM), Excess Predicted Average Crash Frequency is proposed for hot zone identification. Using EPP, a statewide screening exercise by the various vehicle types considered in our analysis was undertaken. The screening results revealed that the spatial pattern of hot zones is substantially different across the various vehicle types considered. Copyright © 2017 Elsevier Ltd. All rights reserved.

Hormesis and Paradoxical Effects of Drooping Birch (Betula pendula Roth) Parameters Under Motor Traffic Pollution

PubMed Central

2015-01-01

Various plant indexes are used or recommended for bioindication. However, the nonmonotonic dose–response dependences (hormesis and paradoxical effects) of these indexes are insufficiently explored upon exposure to pollution. We studied the dependences of these Betula pendula indexes on the intensity of motor traffic pollution. Regression analysis did not reveal any dependence of chlorophyll and carotenoid content on traffic intensity (in 2008 and 2010-2013). Lipid peroxidation rate had different versions of paradoxical effects in 2008 and 2010 to 2012 and increased in comparison with control under an increase in pollution level in 2013. In 2010 to 2012, all dose–response dependences for total protein and thiol group content were biphasic and multiphasic paradoxical effects. In 2013, an increase in traffic intensity induced a linear reduction in protein content and an increase in thiol group level in comparison with the control. In most cases, the studied phenological indexes and seed production decreased monotonically in comparison with the control following an increase in traffic intensity. Only in 2010 and 2013, share of fallen leaves had hormesis and paradoxical effect accordingly. Fluctuating asymmetry had a paradoxical effect and hormesis in 2008 and 2012, accordingly, and increased in comparison with the control under an increase in the level of pollution in 2010 to 2011. PMID:26676071

Nonlinear relative-proportion-based route adjustment process for day-to-day traffic dynamics: modeling, equilibrium and stability analysis

NASA Astrophysics Data System (ADS)

Zhu, Wenlong; Ma, Shoufeng; Tian, Junfang; Li, Geng

2016-11-01

Travelers' route adjustment behaviors in a congested road traffic network are acknowledged as a dynamic game process between them. Existing Proportional-Switch Adjustment Process (PSAP) models have been extensively investigated to characterize travelers' route choice behaviors; PSAP has concise structure and intuitive behavior rule. Unfortunately most of which have some limitations, i.e., the flow over adjustment problem for the discrete PSAP model, the absolute cost differences route adjustment problem, etc. This paper proposes a relative-Proportion-based Route Adjustment Process (rePRAP) maintains the advantages of PSAP and overcomes these limitations. The rePRAP describes the situation that travelers on higher cost route switch to those with lower cost at the rate that is unilaterally depended on the relative cost differences between higher cost route and its alternatives. It is verified to be consistent with the principle of the rational behavior adjustment process. The equivalence among user equilibrium, stationary path flow pattern and stationary link flow pattern is established, which can be applied to judge whether a given network traffic flow has reached UE or not by detecting the stationary or non-stationary state of link flow pattern. The stability theorem is proved by the Lyapunov function approach. A simple example is tested to demonstrate the effectiveness of the rePRAP model.

Effects of traffic noise on the calling behavior of two Neotropical hylid frogs

PubMed Central

Cechin, Sonia; Antunes, Rógger; Borges-Martins, Márcio

2017-01-01

Anthropogenic disturbance has been pointed to as one of the major causes of the world´s biodiversity crisis. Among them, noise pollution is a potential underestimated threat, projected to increase in the next decades accompanying urban expansion. Rising levels of noise pollution may result in negative impacts on species highly dependent on acoustic communication. Amphibians have long served as model organisms for investigating animal acoustic communication because their reproduction depends on transmitting and receiving acoustic signals. A few studies have investigated the effects of anthropogenic noise on anurans, but there is still limited knowledge on how it affects them. In this study, we test the effect of two intensities of traffic noise on calling males of two Neotropical treefrogs species. We expect to record more changes in call parameters, to avoid masking effect, at higher intensity noise treatments, and in the species with higher call/noise frequency overlap. We performed a set of field playback experiments exposing male frogs to road noise at two different intensities (65dB and 75dB). Focal species are Boana bischoffi (high call/noise frequency overlap) and B. leptolineata (low call/noise frequency overlap). Both species changed acoustic parameters during or after the exposure to traffic noise. Advertisement call rate of B. bischoffi decreased during road noise, and dominant frequency decreased over time. Call length of B. leptolineata increased or decreased, depending on the order of noise intensity. We also observed spatial displacement in both species, which moved away from the noise source. Our results provide evidence that traffic noise affects anuran calling behavior, and noise intensity is an important factor affecting how species respond. PMID:28854253

Effects of traffic noise on the calling behavior of two Neotropical hylid frogs.

PubMed

Caorsi, Valentina Zaffaroni; Both, Camila; Cechin, Sonia; Antunes, Rógger; Borges-Martins, Márcio

2017-01-01

Anthropogenic disturbance has been pointed to as one of the major causes of the world´s biodiversity crisis. Among them, noise pollution is a potential underestimated threat, projected to increase in the next decades accompanying urban expansion. Rising levels of noise pollution may result in negative impacts on species highly dependent on acoustic communication. Amphibians have long served as model organisms for investigating animal acoustic communication because their reproduction depends on transmitting and receiving acoustic signals. A few studies have investigated the effects of anthropogenic noise on anurans, but there is still limited knowledge on how it affects them. In this study, we test the effect of two intensities of traffic noise on calling males of two Neotropical treefrogs species. We expect to record more changes in call parameters, to avoid masking effect, at higher intensity noise treatments, and in the species with higher call/noise frequency overlap. We performed a set of field playback experiments exposing male frogs to road noise at two different intensities (65dB and 75dB). Focal species are Boana bischoffi (high call/noise frequency overlap) and B. leptolineata (low call/noise frequency overlap). Both species changed acoustic parameters during or after the exposure to traffic noise. Advertisement call rate of B. bischoffi decreased during road noise, and dominant frequency decreased over time. Call length of B. leptolineata increased or decreased, depending on the order of noise intensity. We also observed spatial displacement in both species, which moved away from the noise source. Our results provide evidence that traffic noise affects anuran calling behavior, and noise intensity is an important factor affecting how species respond.

Chemical compositions and source identification of PM₂.₅ aerosols for estimation of a diesel source surrogate.

PubMed

Sahu, Manoranjan; Hu, Shaohua; Ryan, Patrick H; Le Masters, Grace; Grinshpun, Sergey A; Chow, Judith C; Biswas, Pratim

2011-06-01

Exposure to traffic-related pollution during childhood has been associated with asthma exacerbation, and asthma incidence. The objective of the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS) is to determine if the development of allergic and respiratory disease is associated with exposure to diesel engine exhaust particles. A detailed receptor model analyses was undertaken by applying positive matrix factorization (PMF) and UNMIX receptor models to two PM₂.₅ data sets: one consisting of two carbon fractions and the other of eight temperature-resolved carbon fractions. Based on the source profiles resolved from the analyses, markers of traffic-related air pollution were estimated: the elemental carbon attributed to traffic (ECAT) and elemental carbon attributed to diesel vehicle emission (ECAD). Application of UNMIX to the two data sets generated four source factors: combustion related sulfate, traffic, metal processing and soil/crustal. The PMF application generated six source factors derived from analyzing two carbon fractions and seven factors from temperature-resolved eight carbon fractions. The source factors (with source contribution estimates by mass concentrations in parentheses) are: combustion sulfate (46.8%), vegetative burning (15.8%), secondary sulfate (12.9%), diesel vehicle emission (10.9%), metal processing (7.5%), gasoline vehicle emission (5.6%) and soil/crustal (0.7%). Diesel and gasoline vehicle emission sources were separated using eight temperature-resolved organic and elemental carbon fractions. Application of PMF to both datasets also differentiated the sulfate rich source from the vegetative burning source, which are combined in a single factor by UNMIX modeling. Calculated ECAT and ECAD values at different locations indicated that traffic source impacts depend on factors such as traffic volumes, meteorological parameters, and the mode of vehicle operation apart from the proximity of the sites to highways. The difference in ECAT and ECAD, however, was less than one standard deviation. Thus, a cost benefit consideration should be used when deciding on the benefits of an eight or two carbon approach. Published by Elsevier B.V.

A refined and dynamic cellular automaton model for pedestrian-vehicle mixed traffic flow

NASA Astrophysics Data System (ADS)

Liu, Mianfang; Xiong, Shengwu

2016-12-01

Mixed traffic flow sharing the “same lane” and having no discipline on road is a common phenomenon in the developing countries. For example, motorized vehicles (m-vehicles) and nonmotorized vehicles (nm-vehicles) may share the m-vehicle lane or nm-vehicle lane and pedestrians may share the nm-vehicle lane. Simulating pedestrian-vehicle mixed traffic flow consisting of three kinds of traffic objects: m-vehicles, nm-vehicles and pedestrians, can be a challenge because there are some erratic drivers or pedestrians who fail to follow the lane disciplines. In the paper, we investigate various moving and interactive behavior associated with mixed traffic flow, such as lateral drift including illegal lane-changing and transverse crossing different lanes, overtaking and forward movement, and propose some new moving and interactive rules for pedestrian-vehicle mixed traffic flow based on a refined and dynamic cellular automaton (CA) model. Simulation results indicate that the proposed model can be used to investigate the traffic flow characteristic in a mixed traffic flow system and corresponding complicated traffic problems, such as, the moving characteristics of different traffic objects, interaction phenomenon between different traffic objects, traffic jam, traffic conflict, etc., which are consistent with the actual mixed traffic system. Therefore, the proposed model provides a solid foundation for the management, planning and evacuation of the mixed traffic flow.

Rapid transporter regulation prevents substrate flow traffic jams in boron transport

PubMed Central

Sotta, Naoyuki; Duncan, Susan; Tanaka, Mayuki; Sato, Takafumi

2017-01-01

Nutrient uptake by roots often involves substrate-dependent regulated nutrient transporters. For robust uptake, the system requires a regulatory circuit within cells and a collective, coordinated behaviour across the tissue. A paradigm for such systems is boron uptake, known for its directional transport and homeostasis, as boron is essential for plant growth but toxic at high concentrations. In Arabidopsis thaliana, boron uptake occurs via diffusion facilitators (NIPs) and exporters (BORs), each presenting distinct polarity. Intriguingly, although boron soil concentrations are homogenous and stable, both transporters manifest strikingly swift boron-dependent regulation. Through mathematical modelling, we demonstrate that slower regulation of these transporters leads to physiologically detrimental oscillatory behaviour. Cells become periodically exposed to potentially cytotoxic boron levels, and nutrient throughput to the xylem becomes hampered. We conclude that, while maintaining homeostasis, swift transporter regulation within a polarised tissue context is critical to prevent intrinsic traffic-jam like behaviour of nutrient flow. PMID:28870285

Rapid transporter regulation prevents substrate flow traffic jams in boron transport.

PubMed

Sotta, Naoyuki; Duncan, Susan; Tanaka, Mayuki; Sato, Takafumi; Marée, Athanasius Fm; Fujiwara, Toru; Grieneisen, Verônica A

2017-09-05

Nutrient uptake by roots often involves substrate-dependent regulated nutrient transporters. For robust uptake, the system requires a regulatory circuit within cells and a collective, coordinated behaviour across the tissue. A paradigm for such systems is boron uptake, known for its directional transport and homeostasis, as boron is essential for plant growth but toxic at high concentrations. In Arabidopsis thaliana , boron uptake occurs via diffusion facilitators (NIPs) and exporters (BORs), each presenting distinct polarity. Intriguingly, although boron soil concentrations are homogenous and stable, both transporters manifest strikingly swift boron-dependent regulation. Through mathematical modelling, we demonstrate that slower regulation of these transporters leads to physiologically detrimental oscillatory behaviour. Cells become periodically exposed to potentially cytotoxic boron levels, and nutrient throughput to the xylem becomes hampered. We conclude that, while maintaining homeostasis, swift transporter regulation within a polarised tissue context is critical to prevent intrinsic traffic-jam like behaviour of nutrient flow.

A cellular automaton model for ship traffic flow in waterways

NASA Astrophysics Data System (ADS)

Qi, Le; Zheng, Zhongyi; Gang, Longhui

2017-04-01

With the development of marine traffic, waterways become congested and more complicated traffic phenomena in ship traffic flow are observed. It is important and necessary to build a ship traffic flow model based on cellular automata (CAs) to study the phenomena and improve marine transportation efficiency and safety. Spatial discretization rules for waterways and update rules for ship movement are two important issues that are very different from vehicle traffic. To solve these issues, a CA model for ship traffic flow, called a spatial-logical mapping (SLM) model, is presented. In this model, the spatial discretization rules are improved by adding a mapping rule. And the dynamic ship domain model is considered in the update rules to describe ships' interaction more exactly. Take the ship traffic flow in the Singapore Strait for example, some simulations were carried out and compared. The simulations show that the SLM model could avoid ship pseudo lane-change efficiently, which is caused by traditional spatial discretization rules. The ship velocity change in the SLM model is consistent with the measured data. At finally, from the fundamental diagram, the relationship between traffic ability and the lengths of ships is explored. The number of ships in the waterway declines when the proportion of large ships increases.

Scheduling logic for Miles-In-Trail traffic management

NASA Technical Reports Server (NTRS)

Synnestvedt, Robert G.; Swenson, Harry; Erzberger, Heinz

1995-01-01

This paper presents an algorithm which can be used for scheduling arrival air traffic in an Air Route Traffic Control Center (ARTCC or Center) entering a Terminal Radar Approach Control (TRACON) Facility . The algorithm aids a Traffic Management Coordinator (TMC) in deciding how to restrict traffic while the traffic expected to arrive in the TRACON exceeds the TRACON capacity. The restrictions employed fall under the category of Miles-in-Trail, one of two principal traffic separation techniques used in scheduling arrival traffic . The algorithm calculates aircraft separations for each stream of aircraft destined to the TRACON. The calculations depend upon TRACON characteristics, TMC preferences, and other parameters adapted to the specific needs of scheduling traffic in a Center. Some preliminary results of traffic simulations scheduled by this algorithm are presented, and conclusions are drawn as to the effectiveness of using this algorithm in different traffic scenarios.

78 FR 23626 - Agency Information Collection Activities; Requests for Comments; Clearance of Renewed Approval of...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-19

... Dependent Surveillance--Broadcast (ADS-B) Out Performance Requirements To Support Air Traffic Control (ATC.... The final rule titled ``Automatic Dependent Surveillance-- Broadcast (ADS-B) Equipage Mandate To... System. The rule facilitates the use of ADS-B for aircraft surveillance by FAA air traffic controllers to...

Traffic and Driving Simulator Based on Architecture of Interactive Motion.

PubMed

Paz, Alexander; Veeramisti, Naveen; Khaddar, Romesh; de la Fuente-Mella, Hanns; Modorcea, Luiza

2015-01-01

This study proposes an architecture for an interactive motion-based traffic simulation environment. In order to enhance modeling realism involving actual human beings, the proposed architecture integrates multiple types of simulation, including: (i) motion-based driving simulation, (ii) pedestrian simulation, (iii) motorcycling and bicycling simulation, and (iv) traffic flow simulation. The architecture has been designed to enable the simulation of the entire network; as a result, the actual driver, pedestrian, and bike rider can navigate anywhere in the system. In addition, the background traffic interacts with the actual human beings. This is accomplished by using a hybrid mesomicroscopic traffic flow simulation modeling approach. The mesoscopic traffic flow simulation model loads the results of a user equilibrium traffic assignment solution and propagates the corresponding traffic through the entire system. The microscopic traffic flow simulation model provides background traffic around the vicinities where actual human beings are navigating the system. The two traffic flow simulation models interact continuously to update system conditions based on the interactions between actual humans and the fully simulated entities. Implementation efforts are currently in progress and some preliminary tests of individual components have been conducted. The implementation of the proposed architecture faces significant challenges ranging from multiplatform and multilanguage integration to multievent communication and coordination.

Traffic and Driving Simulator Based on Architecture of Interactive Motion

PubMed Central

Paz, Alexander; Veeramisti, Naveen; Khaddar, Romesh; de la Fuente-Mella, Hanns; Modorcea, Luiza

2015-01-01

This study proposes an architecture for an interactive motion-based traffic simulation environment. In order to enhance modeling realism involving actual human beings, the proposed architecture integrates multiple types of simulation, including: (i) motion-based driving simulation, (ii) pedestrian simulation, (iii) motorcycling and bicycling simulation, and (iv) traffic flow simulation. The architecture has been designed to enable the simulation of the entire network; as a result, the actual driver, pedestrian, and bike rider can navigate anywhere in the system. In addition, the background traffic interacts with the actual human beings. This is accomplished by using a hybrid mesomicroscopic traffic flow simulation modeling approach. The mesoscopic traffic flow simulation model loads the results of a user equilibrium traffic assignment solution and propagates the corresponding traffic through the entire system. The microscopic traffic flow simulation model provides background traffic around the vicinities where actual human beings are navigating the system. The two traffic flow simulation models interact continuously to update system conditions based on the interactions between actual humans and the fully simulated entities. Implementation efforts are currently in progress and some preliminary tests of individual components have been conducted. The implementation of the proposed architecture faces significant challenges ranging from multiplatform and multilanguage integration to multievent communication and coordination. PMID:26491711

Assessment of traffic noise levels in urban areas using different soft computing techniques.

PubMed

Tomić, J; Bogojević, N; Pljakić, M; Šumarac-Pavlović, D

2016-10-01

Available traffic noise prediction models are usually based on regression analysis of experimental data, and this paper presents the application of soft computing techniques in traffic noise prediction. Two mathematical models are proposed and their predictions are compared to data collected by traffic noise monitoring in urban areas, as well as to predictions of commonly used traffic noise models. The results show that application of evolutionary algorithms and neural networks may improve process of development, as well as accuracy of traffic noise prediction.

Modelling traffic air pollution in road tunnels

NASA Astrophysics Data System (ADS)

Bellasio, Roberto

This paper presents two models for the description of air pollutant concentrations in road tunnels due to traffic. Turbulence is assumed to depend on both atmospheric turbulence and vehicle's motion. Emissions are calculated with the COPERT90 methodology (Eggleston et al., 1991). Up to 34 different vehicle categories can be considered at the same time, with an arbitrary number of vehicles travelling inside the tunnel. Emissions are calculated as a function of the position inside the tunnel and of the time. Three pollutants can be simulated with the current version of the models, CO, NO x and VOC. It is also possible to consider vehicles with null emissions. The models are able to consider the effects of an arbitrary number of sinks. Flow rates and outdoor concentrations are a function of the time for each sink. The equation of conservation of mass has been solved with the control volumes method. Particular attention has been given to the formulation of stability conditions. Sensitivity analysis was conducted to verify the model answer to different input parameters such as initial concentration, boundary concentrations and vehicle-induced turbulence. Examples of application are given for a tunnel with urban traffic regime, including passenger cars with different fuels, light duty trucks, heavy duty trucks and motorbikes, and for an underground railway. Simulations have been carried out for the five working days of the week.

Construction and simulation of a novel continuous traffic flow model

NASA Astrophysics Data System (ADS)

Hwang, Yao-Hsin; Yu, Jui-Ling

2017-12-01

In this paper, we aim to propose a novel mathematical model for traffic flow and apply a newly developed characteristic particle method to solve the associate governing equations. As compared with the existing non-equilibrium higher-order traffic flow models, the present one is put forward to satisfy the following three conditions: Preserve the equilibrium state in the smooth region. Yield an anisotropic propagation of traffic flow information. Expressed with a conservation law form for traffic momentum. These conditions will ensure a more practical simulation in traffic flow physics: The current traffic will not be influenced by the condition in the behind and result in unambiguous condition across a traffic shock. Through analyses of characteristics, stability condition and steady-state solution adherent to the equation system, it is shown that the proposed model actually conform to these conditions. Furthermore, this model can be cast into its characteristic form which, incorporated with the Rankine-Hugoniot relation, is appropriate to be simulated by the characteristic particle method to obtain accurate computational results.

Comparing exposure assessment methods for traffic-related air pollution in an adverse pregnancy outcome study.

PubMed

Wu, Jun; Wilhelm, Michelle; Chung, Judith; Ritz, Beate

2011-07-01

Previous studies reported adverse impacts of traffic-related air pollution exposure on pregnancy outcomes. Yet, little information exists on how effect estimates are impacted by the different exposure assessment methods employed in these studies. To compare effect estimates for traffic-related air pollution exposure and preeclampsia, preterm birth (gestational age less than 37 weeks), and very preterm birth (gestational age less than 30 weeks) based on four commonly used exposure assessment methods. We identified 81,186 singleton births during 1997-2006 at four hospitals in Los Angeles and Orange Counties, California. Exposures were assigned to individual subjects based on residential address at delivery using the nearest ambient monitoring station data [carbon monoxide (CO), nitrogen dioxide (NO(2)), nitric oxide (NO), nitrogen oxides (NO(x)), ozone (O(3)), and particulate matter less than 2.5 (PM(2.5)) or less than 10 (PM(10))μm in aerodynamic diameter], both unadjusted and temporally adjusted land-use regression (LUR) model estimates (NO, NO(2), and NO(x)), CALINE4 line-source air dispersion model estimates (NO(x) and PM(2.5)), and a simple traffic-density measure. We employed unconditional logistic regression to analyze preeclampsia in our birth cohort, while for gestational age-matched risk sets with preterm and very preterm birth we employed conditional logistic regression. We observed elevated risks for preeclampsia, preterm birth, and very preterm birth from maternal exposures to traffic air pollutants measured at ambient stations (CO, NO, NO(2), and NO(x)) and modeled through CALINE4 (NO(x) and PM(2.5)) and LUR (NO(2) and NO(x)). Increased risk of preterm birth and very preterm birth were also positively associated with PM(10) and PM(2.5) air pollution measured at ambient stations. For LUR-modeled NO(2) and NO(x) exposures, elevated risks for all the outcomes were observed in Los Angeles only--the region for which the LUR models were initially developed. Unadjusted LUR models often produced odds ratios somewhat larger in size than temporally adjusted models. The size of effect estimates was smaller for exposures based on simpler traffic density measures than the other exposure assessment methods. We generally confirmed that traffic-related air pollution was associated with adverse reproductive outcomes regardless of the exposure assessment method employed, yet the size of the estimated effect depended on how both temporal and spatial variations were incorporated into exposure assessment. The LUR model was not transferable even between two contiguous areas within the same large metropolitan area in Southern California. Copyright © 2011 Elsevier Inc. All rights reserved.

Hierarchical and coupling model of factors influencing vessel traffic flow.

PubMed

Liu, Zhao; Liu, Jingxian; Li, Huanhuan; Li, Zongzhi; Tan, Zhirong; Liu, Ryan Wen; Liu, Yi

2017-01-01

Understanding the characteristics of vessel traffic flow is crucial in maintaining navigation safety, efficiency, and overall waterway transportation management. Factors influencing vessel traffic flow possess diverse features such as hierarchy, uncertainty, nonlinearity, complexity, and interdependency. To reveal the impact mechanism of the factors influencing vessel traffic flow, a hierarchical model and a coupling model are proposed in this study based on the interpretative structural modeling method. The hierarchical model explains the hierarchies and relationships of the factors using a graph. The coupling model provides a quantitative method that explores interaction effects of factors using a coupling coefficient. The coupling coefficient is obtained by determining the quantitative indicators of the factors and their weights. Thereafter, the data obtained from Port of Tianjin is used to verify the proposed coupling model. The results show that the hierarchical model of the factors influencing vessel traffic flow can explain the level, structure, and interaction effect of the factors; the coupling model is efficient in analyzing factors influencing traffic volumes. The proposed method can be used for analyzing increases in vessel traffic flow in waterway transportation system.

Hierarchical and coupling model of factors influencing vessel traffic flow

PubMed Central

Liu, Jingxian; Li, Huanhuan; Li, Zongzhi; Tan, Zhirong; Liu, Ryan Wen; Liu, Yi

2017-01-01

Understanding the characteristics of vessel traffic flow is crucial in maintaining navigation safety, efficiency, and overall waterway transportation management. Factors influencing vessel traffic flow possess diverse features such as hierarchy, uncertainty, nonlinearity, complexity, and interdependency. To reveal the impact mechanism of the factors influencing vessel traffic flow, a hierarchical model and a coupling model are proposed in this study based on the interpretative structural modeling method. The hierarchical model explains the hierarchies and relationships of the factors using a graph. The coupling model provides a quantitative method that explores interaction effects of factors using a coupling coefficient. The coupling coefficient is obtained by determining the quantitative indicators of the factors and their weights. Thereafter, the data obtained from Port of Tianjin is used to verify the proposed coupling model. The results show that the hierarchical model of the factors influencing vessel traffic flow can explain the level, structure, and interaction effect of the factors; the coupling model is efficient in analyzing factors influencing traffic volumes. The proposed method can be used for analyzing increases in vessel traffic flow in waterway transportation system. PMID:28414747

Lattice hydrodynamic model based traffic control: A transportation cyber-physical system approach

NASA Astrophysics Data System (ADS)

Liu, Hui; Sun, Dihua; Liu, Weining

2016-11-01

Lattice hydrodynamic model is a typical continuum traffic flow model, which describes the jamming transition of traffic flow properly. Previous studies in lattice hydrodynamic model have shown that the use of control method has the potential to improve traffic conditions. In this paper, a new control method is applied in lattice hydrodynamic model from a transportation cyber-physical system approach, in which only one lattice site needs to be controlled in this control scheme. The simulation verifies the feasibility and validity of this method, which can ensure the efficient and smooth operation of the traffic flow.

Estimated long-term outdoor air pollution concentrations in a cohort study

NASA Astrophysics Data System (ADS)

Beelen, Rob; Hoek, Gerard; Fischer, Paul; Brandt, Piet A. van den; Brunekreef, Bert

Several recent studies associated long-term exposure to air pollution with increased mortality. An ongoing cohort study, the Netherlands Cohort Study on Diet and Cancer (NLCS), was used to study the association between long-term exposure to traffic-related air pollution and mortality. Following on a previous exposure assessment study in the NLCS, we improved the exposure assessment methods. Long-term exposure to nitrogen dioxide (NO 2), nitrogen oxide (NO), black smoke (BS), and sulphur dioxide (SO 2) was estimated. Exposure at each home address ( N=21 868) was considered as a function of a regional, an urban and a local component. The regional component was estimated using inverse distance weighed interpolation of measurement data from regional background sites in a national monitoring network. Regression models with urban concentrations as dependent variables, and number of inhabitants in different buffers and land use variables, derived with a Geographic Information System (GIS), as predictor variables were used to estimate the urban component. The local component was assessed using a GIS and a digital road network with linked traffic intensities. Traffic intensity on the nearest road and on the nearest major road, and the sum of traffic intensity in a buffer of 100 m around each home address were assessed. Further, a quantitative estimate of the local component was estimated. The regression models to estimate the urban component explained 67%, 46%, 49% and 35% of the variances of NO 2, NO, BS, and SO 2 concentrations, respectively. Overall regression models which incorporated the regional, urban and local component explained 84%, 44%, 59% and 56% of the variability in concentrations for NO 2, NO, BS and SO 2, respectively. We were able to develop an exposure assessment model using GIS methods and traffic intensities that explained a large part of the variations in outdoor air pollution concentrations.

Traffic analysis toolbox volume XI : weather and traffic analysis, modeling and simulation.

DOT National Transportation Integrated Search

2010-12-01

This document presents a weather module for the traffic analysis tools program. It provides traffic engineers, transportation modelers and decisions makers with a guide that can incorporate weather impacts into transportation system analysis and mode...

Transportation data requirements : evaluation of portable traffic recorders.

DOT National Transportation Integrated Search

1978-01-01

The objective of this study was to evaluate the accuracy of the Department's portable traffic recorder models under diverse types of traffic conditions. The study was conducted by (1) reviewing the characteristics of five models of traffic recorders,...

Traffic flow behavior at a single-lane urban roundabout

NASA Astrophysics Data System (ADS)

Lakouari, N.; Oubram, O.; Ez-Zahraouy, H.; Cisneros-Villalobos, L.; Velásquez-Aguilar, J. G.

In this paper, we propose a stochastic cellular automata model to study the traffic behavior at a single-lane roundabout. Vehicles can enter the interior lane or exit from it via N intersecting lane, the boundary conditions are stochastic. The traffic is controlled by a self-organized scheme. It has turned out that depending on the rules of insertion to the roundabout, five distinct traffic phases can appear, namely, free flow, congestion, maximum current, jammed and gridlock. The transition between the free flow and the gridlock is forbidden. The density profiles are used to study the traffic pattern at the interior lane of the roundabout. In order to quantify the interactions between vehicles in the interior lane of the roundabout, the velocity correlation coefficient (VCC) is also studied. Besides, the spatiotemporal diagrams corresponding to the entry/exit lanes are derived numerically. Furthermore, we have investigated the effect of displaying signal (PIn), as the PIn decreases, the maximum current increases at the expense of the free flow and the jamming phase. Finally, we have investigated the effect of the braking probability P on the interior lane of the roundabout. We have found that the increase of P raises the spontaneous jam formation on the ring. Thus, enlarges the maximum current and the jamming phase while the free flow phase decreases.

Minimal Traffic Model with Safe Driving Conditions

NASA Astrophysics Data System (ADS)

Terborg, Heinrich; Pérez, Luis A.

We have developed a new computational traffic model in which security aspects are fundamental. In this paper we show that this model reproduces many known empirical aspects of vehicular traffic such as the three states of traffic flow and the backward speed of the downstream front of a traffic jam (C), without the aid of adjustable parameters. The model is studied for both open and closed single lane traffic systems. Also, we were able to analytically compute the value of C as 15.37 km/h from a relation that only includes the human reaction time, the mean vehicle length and the effective friction coefficient during the braking process of a vehicle as its main components.

Unsupervised Ensemble Anomaly Detection Using Time-Periodic Packet Sampling

NASA Astrophysics Data System (ADS)

Uchida, Masato; Nawata, Shuichi; Gu, Yu; Tsuru, Masato; Oie, Yuji

We propose an anomaly detection method for finding patterns in network traffic that do not conform to legitimate (i.e., normal) behavior. The proposed method trains a baseline model describing the normal behavior of network traffic without using manually labeled traffic data. The trained baseline model is used as the basis for comparison with the audit network traffic. This anomaly detection works in an unsupervised manner through the use of time-periodic packet sampling, which is used in a manner that differs from its intended purpose — the lossy nature of packet sampling is used to extract normal packets from the unlabeled original traffic data. Evaluation using actual traffic traces showed that the proposed method has false positive and false negative rates in the detection of anomalies regarding TCP SYN packets comparable to those of a conventional method that uses manually labeled traffic data to train the baseline model. Performance variation due to the probabilistic nature of sampled traffic data is mitigated by using ensemble anomaly detection that collectively exploits multiple baseline models in parallel. Alarm sensitivity is adjusted for the intended use by using maximum- and minimum-based anomaly detection that effectively take advantage of the performance variations among the multiple baseline models. Testing using actual traffic traces showed that the proposed anomaly detection method performs as well as one using manually labeled traffic data and better than one using randomly sampled (unlabeled) traffic data.

Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach.

PubMed

Yang, Hao-Fan; Dillon, Tharam S; Chen, Yi-Ping Phoebe

2017-10-01

Forecasting accuracy is an important issue for successful intelligent traffic management, especially in the domain of traffic efficiency and congestion reduction. The dawning of the big data era brings opportunities to greatly improve prediction accuracy. In this paper, we propose a novel model, stacked autoencoder Levenberg-Marquardt model, which is a type of deep architecture of neural network approach aiming to improve forecasting accuracy. The proposed model is designed using the Taguchi method to develop an optimized structure and to learn traffic flow features through layer-by-layer feature granulation with a greedy layerwise unsupervised learning algorithm. It is applied to real-world data collected from the M6 freeway in the U.K. and is compared with three existing traffic predictors. To the best of our knowledge, this is the first time that an optimized structure of the traffic flow forecasting model with a deep learning approach is presented. The evaluation results demonstrate that the proposed model with an optimized structure has superior performance in traffic flow forecasting.

Cellular automata model for traffic flow at intersections in internet of vehicles

NASA Astrophysics Data System (ADS)

Zhao, Han-Tao; Liu, Xin-Ru; Chen, Xiao-Xu; Lu, Jian-Cheng

2018-03-01

Considering the effect of the front vehicle's speed, the influence of the brake light and the conflict of the traffic flow, we established a cellular automata model called CE-NS for traffic flow at the intersection in the non-vehicle networking environment. According to the information interaction of Internet of Vehicles (IoV), introducing parameters describing the congestion and the accurate speed of the front vehicle into the CE-NS model, we improved the rules of acceleration, deceleration and conflict, and finally established a cellular automata model for traffic flow at intersections of IoV. The relationship between traffic parameters such as vehicle speed, flow and average travel time is obtained by numerical simulation of two models. Based on this, we compared the traffic situation of the non-vehicle networking environment with conditions of IoV environment, and analyzed the influence of the different degree of IoV on the traffic flow. The results show that the traffic speed is increased, the travel time is reduced, the flux of intersections is increased and the traffic flow is more smoothly under IoV environment. After the vehicle which achieves IoV reaches a certain proportion, the operation effect of the traffic flow begins to improve obviously.

A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

NASA Astrophysics Data System (ADS)

Huang, Darong; Bai, Xing-Rong

Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.

Evaluation of the absorption Ångström exponents for traffic and wood burning in the Aethalometer-based source apportionment using radiocarbon measurements of ambient aerosol

NASA Astrophysics Data System (ADS)

Zotter, Peter; Herich, Hanna; Gysel, Martin; El-Haddad, Imad; Zhang, Yanlin; Močnik, Griša; Hüglin, Christoph; Baltensperger, Urs; Szidat, Sönke; Prévôt, André S. H.

2017-03-01

Equivalent black carbon (EBC) measured by a multi-wavelength Aethalometer can be apportioned to traffic and wood burning. The method is based on the differences in the dependence of aerosol absorption on the wavelength of light used to investigate the sample, parameterized by the source-specific absorption Ångström exponent (α). While the spectral dependence (defined as α values) of the traffic-related EBC light absorption is low, wood smoke particles feature enhanced light absorption in the blue and near ultraviolet. Source apportionment results using this methodology are hence strongly dependent on the α values assumed for both types of emissions: traffic αTR, and wood burning αWB. Most studies use a single αTR and αWB pair in the Aethalometer model, derived from previous work. However, an accurate determination of the source specific α values is currently lacking and in some recent publications the applicability of the Aethalometer model was questioned.Here we present an indirect methodology for the determination of αWB and αTR by comparing the source apportionment of EBC using the Aethalometer model with 14C measurements of the EC fraction on 16 to 40 h filter samples from several locations and campaigns across Switzerland during 2005-2012, mainly in winter. The data obtained at eight stations with different source characteristics also enabled the evaluation of the performance and the uncertainties of the Aethalometer model in different environments. The best combination of αTR and αWB (0.9 and 1.68, respectively) was obtained by fitting the Aethalometer model outputs (calculated with the absorption coefficients at 470 and 950 nm) against the fossil fraction of EC (ECF / EC) derived from 14C measurements. Aethalometer and 14C source apportionment results are well correlated (r = 0.81) and the fitting residuals exhibit only a minor positive bias of 1.6 % and an average precision of 9.3 %. This indicates that the Aethalometer model reproduces reasonably well the 14C results for all stations investigated in this study using our best estimate of a single αWB and αTR pair. Combining the EC, 14C, and Aethalometer measurements further allowed assessing the dependence of the mass absorption cross section (MAC) of EBC on its source. Results indicate no significant difference in MAC at 880 nm between EBC originating from traffic or wood-burning emissions. Using ECF / EC as reference and constant a priori selected αTR values, αWB was also calculated for each individual data point. No clear station-to-station or season-to-season differences in αWB were observed, but αTR and αWB values are interdependent. For example, an increase in αTR by 0.1 results in a decrease in αWB by 0.1. The fitting residuals of different αTR and αWB combinations depend on ECF / EC such that a good agreement cannot be obtained over the entire ECF / EC range using other α pairs. Additional combinations of αTR = 0.8, and 1.0 and αWB = 1.8 and 1.6, respectively, are possible but only for ECF / EC between ˜ 40 and 85 %. Applying α values previously used in the literature such as αWB of ˜ 2 or any αWB in combination with αTR = 1.1 to our data set results in large residuals. Therefore we recommend to use the best α combination as obtained here (αTR = 0.9 and αWB = 1.68) in future studies when no or only limited additional information like 14C measurements are available. However, these results were obtained for locations impacted by black carbon (BC) mainly from traffic consisting of a modern car fleet and residential wood combustion with well-constrained combustion efficiencies. For regions of the world with different combustion conditions, additional BC sources, or fuels used, further investigations are needed.

A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data.

PubMed

Wang, Xiaomeng; Peng, Ling; Chi, Tianhe; Li, Mengzhu; Yao, Xiaojing; Shao, Jing

2015-01-01

Urban-scale traffic monitoring plays a vital role in reducing traffic congestion. Owing to its low cost and wide coverage, floating car data (FCD) serves as a novel approach to collecting traffic data. However, sparse probe data represents the vast majority of the data available on arterial roads in most urban environments. In order to overcome the problem of data sparseness, this paper proposes a hidden Markov model (HMM)-based traffic estimation model, in which the traffic condition on a road segment is considered as a hidden state that can be estimated according to the conditions of road segments having similar traffic characteristics. An algorithm based on clustering and pattern mining rather than on adjacency relationships is proposed to find clusters with road segments having similar traffic characteristics. A multi-clustering strategy is adopted to achieve a trade-off between clustering accuracy and coverage. Finally, the proposed model is designed and implemented on the basis of a real-time algorithm. Results of experiments based on real FCD confirm the applicability, accuracy, and efficiency of the model. In addition, the results indicate that the model is practicable for traffic estimation on urban arterials and works well even when more than 70% of the probe data are missing.

Building the vision, a series of AZTech ITS model deployment success stories for the Phoenix metropolitan area : number one : seizing opportunity : discovering a cost-effective solution for linking traffic centers

DOT National Transportation Integrated Search

1998-01-01

To achieve its goal of integrating the intelligent transportation infrastructure throughout the Valley of the Sun, AZTech had to build links. AZTech's success is highly dependent on its ability to establish strong links throughout its partnership of ...

A multiclass vehicular dynamic traffic flow model for main roads and dedicated lanes/roads of multimodal transport network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sossoe, K.S., E-mail: kwami.sossoe@irt-systemx.fr; Lebacque, J-P., E-mail: jean-patrick.lebacque@ifsttar.fr

2015-03-10

We present in this paper a model of vehicular traffic flow for a multimodal transportation road network. We introduce the notion of class of vehicles to refer to vehicles of different transport modes. Our model describes the traffic on highways (which may contain several lanes) and network transit for pubic transportation. The model is drafted with Eulerian and Lagrangian coordinates and uses a Logit model to describe the traffic assignment of our multiclass vehicular flow description on shared roads. The paper also discusses traffic streams on dedicated lanes for specific class of vehicles with event-based traffic laws. An Euler-Lagrangian-remap schememore » is introduced to numerically approximate the model’s flow equations.« less

Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model.

PubMed

Zhang, Xujun; Pang, Yuanyuan; Cui, Mengjing; Stallones, Lorann; Xiang, Huiyun

2015-02-01

Road traffic injuries have become a major public health problem in China. This study aimed to develop statistical models for predicting road traffic deaths and to analyze seasonality of deaths in China. A seasonal autoregressive integrated moving average (SARIMA) model was used to fit the data from 2000 to 2011. Akaike Information Criterion, Bayesian Information Criterion, and mean absolute percentage error were used to evaluate the constructed models. Autocorrelation function and partial autocorrelation function of residuals and Ljung-Box test were used to compare the goodness-of-fit between the different models. The SARIMA model was used to forecast monthly road traffic deaths in 2012. The seasonal pattern of road traffic mortality data was statistically significant in China. SARIMA (1, 1, 1) (0, 1, 1)12 model was the best fitting model among various candidate models; the Akaike Information Criterion, Bayesian Information Criterion, and mean absolute percentage error were -483.679, -475.053, and 4.937, respectively. Goodness-of-fit testing showed nonautocorrelations in the residuals of the model (Ljung-Box test, Q = 4.86, P = .993). The fitted deaths using the SARIMA (1, 1, 1) (0, 1, 1)12 model for years 2000 to 2011 closely followed the observed number of road traffic deaths for the same years. The predicted and observed deaths were also very close for 2012. This study suggests that accurate forecasting of road traffic death incidence is possible using SARIMA model. The SARIMA model applied to historical road traffic deaths data could provide important evidence of burden of road traffic injuries in China. Copyright © 2015 Elsevier Inc. All rights reserved.

Transportation noise and annoyance related to road traffic in the French RECORD study

PubMed Central

2013-01-01

Road traffic and related noise is a major source of annoyance and impairment to health in urban areas. Many areas exposed to road traffic noise are also exposed to rail and air traffic noise. The resulting annoyance may depend on individual/neighborhood socio-demographic factors. Nevertheless, few studies have taken into account the confounding or modifying factors in the relationship between transportation noise and annoyance due to road traffic. In this study, we address these issues by combining Geographic Information Systems and epidemiologic methods. Street network buffers with a radius of 500 m were defined around the place of residence of the 7290 participants of the RECORD Cohort in Ile-de-France. Estimated outdoor traffic noise levels (road, rail, and air separately) were assessed at each place of residence and in each of these buffers. Higher levels of exposure to noise were documented in low educated neighborhoods. Multilevel logistic regression models documented positive associations between road traffic noise and annoyance due to road traffic, after adjusting for individual/neighborhood socioeconomic conditions. There was no evidence that the association was of different magnitude when noise was measured at the place of residence or in the residential neighborhood. However, the strength of the association between neighborhood noise exposure and annoyance increased when considering a higher percentile in the distribution of noise in each neighborhood. Road traffic noise estimated at the place of residence and road traffic noise in the residential neighborhood (75th percentile) were independently associated with annoyance, when adjusted for each other. Interactions of effects indicated that the relationship between road traffic noise exposure in the residential neighborhood and annoyance was stronger in affluent and high educated neighborhoods. Overall, our findings suggest that it is useful to take into account (i) the exposure to transportation noise in the residential neighborhood rather than only at the residence, (ii) different percentiles of noise exposure in the residential neighborhood, and (iii) the socioeconomic characteristics of the residential neighborhood to explain variations in annoyance due to road traffic in the neighborhood. PMID:24088229

Benefits of Imperfect Conflict Resolution Advisory Aids for Future Air Traffic Control.

PubMed

Trapsilawati, Fitri; Wickens, Christopher D; Qu, Xingda; Chen, Chun-Hsien

2016-11-01

The aim of this study was to examine the human-automation interaction issues and the interacting factors in the context of conflict detection and resolution advisory (CRA) systems. The issues of imperfect automation in air traffic control (ATC) have been well documented in previous studies, particularly in conflict-alerting systems. The extent to which the prior findings can be applied to an integrated conflict detection and resolution system in future ATC remains unknown. Twenty-four participants were evenly divided into two groups corresponding to a medium- and a high-traffic density condition, respectively. In each traffic density condition, participants were instructed to perform simulated ATC tasks under four automation conditions, including reliable, unreliable with short time allowance to secondary conflict (TAS), unreliable with long TAS, and manual conditions. Dependent variables accounted for conflict resolution performance, workload, situation awareness, and trust in and dependence on the CRA aid, respectively. Imposing the CRA automation did increase performance and reduce workload as compared with manual performance. The CRA aid did not decrease situation awareness. The benefits of the CRA aid were manifest even when it was imperfectly reliable and were apparent across traffic loads. In the unreliable blocks, trust in the CRA aid was degraded but dependence was not influenced, yet the performance was not adversely affected. The use of CRA aid would benefit ATC operations across traffic densities. CRA aid offers benefits across traffic densities, regardless of its imperfection, as long as its reliability level is set above the threshold of assistance, suggesting its application for future ATC. © 2016, Human Factors and Ergonomics Society.

Multiple-vehicle collision induced by a sudden stop in traffic flow

NASA Astrophysics Data System (ADS)

Sugiyama, Naoki; Nagatani, Takashi

2012-04-01

We study the dynamic process of the multiple-vehicle collision when a vehicle stops suddenly in a traffic flow. We apply the optimal-velocity model to the vehicular motion. If a vehicle does not decelerate successfully, it crashes into the vehicle ahead with a residual speed. The collision criterion is presented by vi(t)/Δxi(t)→∞ if Δxi(t)→0 where vi(t) and Δxi(t) are the speed and headway of vehicle i at time t. The number of crumpled vehicles depends on the initial velocity, the sensitivity, and the initial headway. We derive the region map (or phase diagram) for the multiple-vehicle collision.

Indicators of residential traffic exposure: Modelled NOX, traffic proximity, and self-reported exposure in RHINE III

NASA Astrophysics Data System (ADS)

Carlsen, Hanne Krage; Bäck, Erik; Eneroth, Kristina; Gislason, Thorarinn; Holm, Mathias; Janson, Christer; Jensen, Steen Solvang; Johannessen, Ane; Kaasik, Marko; Modig, Lars; Segersson, David; Sigsgaard, Torben; Forsberg, Bertil; Olsson, David; Orru, Hans

2017-10-01

Few studies have investigated associations between self-reported and modelled exposure to traffic pollution. The objective of this study was to examine correlations between self-reported traffic exposure and modelled (a) NOX and (b) traffic proximity in seven different northern European cities; Aarhus (Denmark), Bergen (Norway), Gothenburg, Umeå, and Uppsala (Sweden), Reykjavik (Iceland), and Tartu (Estonia). We analysed data from the RHINE III (Respiratory Health in Northern Europe, http://www.rhine.nu)

Network Level Carbon Dioxide Emissions From On-road Sources in the Portland OR, (USA) Metropolitan Area

NASA Astrophysics Data System (ADS)

Powell, J.; Butenhoff, C. L.; Rice, A. L.

2014-12-01

To mitigate climate change, governments at multiple levels are developing policies to decrease anthropogenic carbon dioxide (CO2) emissions. The City of Portland (Oregon) and Multnomah County have adopted a Climate Action Plan with a stated goal of reducing emissions to 80% below 1990 levels by 2050. The transportation sector alone accounts for about 40% of total emissions in the Portland metropolitan area. Here we show a new street-level model of on-road mobile CO2 emissions for the Portland, OR metropolitan region. The model uses hourly traffic counter recordings made by the Portland Bureau of Transportation at 9,352 sites over 21 years (1986-2006), augmented with freeway loop detector data from the Portland Regional Transportation Archive Listing (PORTAL) transportation data archive. We constructed a land use regression model to fill in traffic network gaps with traffic counts as the dependent variable using GIS data such as road class (32 categories) and population density. The Environmental Protection Agency (EPA) MOtor Vehicle Emission Simulator (MOVES) model was used to estimate transportation CO2 emissions. The street-level emissions can be aggregated and gridded and used as input to atmospheric transport models for comparison with atmospheric measurements. This model also provides an independent assessment of top-down inventories that determine emissions from fuel sales, while being an important component of our ongoing effort to assess the effectiveness of emission mitigation strategies at the urban scale.

An adaptable neural-network model for recursive nonlinear traffic prediction and modeling of MPEG video sources.

PubMed

Doulamis, A D; Doulamis, N D; Kollias, S D

2003-01-01

Multimedia services and especially digital video is expected to be the major traffic component transmitted over communication networks [such as internet protocol (IP)-based networks]. For this reason, traffic characterization and modeling of such services are required for an efficient network operation. The generated models can be used as traffic rate predictors, during the network operation phase (online traffic modeling), or as video generators for estimating the network resources, during the network design phase (offline traffic modeling). In this paper, an adaptable neural-network architecture is proposed covering both cases. The scheme is based on an efficient recursive weight estimation algorithm, which adapts the network response to current conditions. In particular, the algorithm updates the network weights so that 1) the network output, after the adaptation, is approximately equal to current bit rates (current traffic statistics) and 2) a minimal degradation over the obtained network knowledge is provided. It can be shown that the proposed adaptable neural-network architecture simulates a recursive nonlinear autoregressive model (RNAR) similar to the notation used in the linear case. The algorithm presents low computational complexity and high efficiency in tracking traffic rates in contrast to conventional retraining schemes. Furthermore, for the problem of offline traffic modeling, a novel correlation mechanism is proposed for capturing the burstness of the actual MPEG video traffic. The performance of the model is evaluated using several real-life MPEG coded video sources of long duration and compared with other linear/nonlinear techniques used for both cases. The results indicate that the proposed adaptable neural-network architecture presents better performance than other examined techniques.

Modeling pedestrian violation behavior at signalized crosswalks in China: a hazards-based duration approach.

PubMed

Guo, Hongwei; Gao, Ziyou; Yang, Xiaobao; Jiang, Xiaobei

2011-02-01

Pedestrian violation is a major cause of traffic accidents involving pedestrians. The research objectives were to investigate the relationship between waiting duration and pedestrian violation and to provide a qualitative and quantitative analysis of the effects of human factors and external environmental factors on street-crossing behavior. Pedestrians' street-crossing behavior was examined by modeling the waiting duration at signalized crosswalk. Pedestrian waiting duration was collected by video cameras and it was assigned as censored and uncensored data to distinguish between normal crossing and violating crossing. A nonparametric baseline duration model was introduced, and variables revealing personal characteristics, traffic conditions, and trip features were defined as covariates to describe the effects of internal and external factors. Pedestrians' crossing behaviors represented positive duration dependence that the longer the waiting time elapsed the more likely pedestrians would end the wait soon. The violation inclination of most pedestrians increased with the increasing waiting duration, but about 10 percent of pedestrians were at high risk of violation to cross the street. About half of pedestrians would still obey the traffic rules even after waiting for 50 s by the street. Human factors and the external environment played an important role in street-crossing behavior, especially for factors that involved pedestrians' subjective willingness. The street-crossing behavior of pedestrians was time dependent. Pedestrians behave differently under the effects of various factors. Pedestrian safety interventions that aim at reducing pedestrian injuries may need to consider these effects. The pedestrians' behavioral modifications, such as enhancing the safety awareness, might be the most efficient means to reducing the likelihood of pedestrian violation, though environmental modifications also worked well in improving pedestrian safety.

Traffic signal synchronization in the saturated high-density grid road network.

PubMed

Hu, Xiaojian; Lu, Jian; Wang, Wei; Zhirui, Ye

2015-01-01

Most existing traffic signal synchronization strategies do not perform well in the saturated high-density grid road network (HGRN). Traffic congestion often occurs in the saturated HGRN, and the mobility of the network is difficult to restore. In order to alleviate traffic congestion and to improve traffic efficiency in the network, the study proposes a regional traffic signal synchronization strategy, named the long green and long red (LGLR) traffic signal synchronization strategy. The essence of the strategy is to control the formation and dissipation of queues and to maximize the efficiency of traffic flows at signalized intersections in the saturated HGRN. With this strategy, the same signal control timing plan is used at all signalized intersections in the HGRN, and the straight phase of the control timing plan has a long green time and a long red time. Therefore, continuous traffic flows can be maintained when vehicles travel, and traffic congestion can be alleviated when vehicles stop. Using the strategy, the LGLR traffic signal synchronization model is developed, with the objective of minimizing the number of stops. Finally, the simulation is executed to analyze the performance of the model by comparing it to other models, and the superiority of the LGLR model is evident in terms of delay, number of stops, queue length, and overall performance in the saturated HGRN.

Traffic jam dynamics in stochastic cellular automata

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nagel, K.; Schreckenberg, M.

1995-09-01

Simple models for particles hopping on a grid (cellular automata) are used to simulate (single lane) traffic flow. Despite their simplicity, these models are astonishingly realistic in reproducing start-stop-waves and realistic fundamental diagrams. One can use these models to investigate traffic phenomena near maximum flow. A so-called phase transition at average maximum flow is visible in the life-times of jams. The resulting dynamic picture is consistent with recent fluid-dynamical results by Kuehne/Kerner/Konhaeuser, and with Treiterer`s hysteresis description. This places CA models between car-following models and fluid-dynamical models for traffic flow. CA models are tested in projects in Los Alamos (USA)more » and in NRW (Germany) for large scale microsimulations of network traffic.« less

The Conception Approach to the Traffic Control in Czech Cities - Examples from Prague

NASA Astrophysics Data System (ADS)

Tichý, Tomáš; Krajčír, Dušan

Modern and economic development of contemporary towns is without question highly dependent upon traffic infrastructure progress. Automobile transport intensity is dramatically rising in large towns and other Czech and European cities. At the same time number of traffic congestions and accidents is increasing, standing times are becoming longer and ecological stress is also escalated. To solve this situation seems to be the most effective solution to design intelligent traffic light intersection control system, variable message signs, preference of public transportation, road line traffic control and next telematics subsystems. This control system and subsystems should improve permeability of traffic road network with a respect for all demands on recent trends of traffic development in towns and regions.

Geosynchronous platform definition study. Volume 4, Part 2: Traffic analysis and system requirements for the new traffic model

NASA Technical Reports Server (NTRS)

1973-01-01

A condensed summary of the traffic analyses and systems requirements for the new traffic model is presented. The results of each study activity are explained, key analyses are described, and important results are highlighted.

Designing Two-Layer Optical Networks with Statistical Multiplexing

NASA Astrophysics Data System (ADS)

Addis, B.; Capone, A.; Carello, G.; Malucelli, F.; Fumagalli, M.; Pedrin Elli, E.

The possibility of adding multi-protocol label switching (MPLS) support to transport networks is considered an important opportunity by telecom carriers that want to add packet services and applications to their networks. However, the question that arises is whether it is suitable to have MPLS nodes just at the edge of the network to collect packet traffic from users, or also to introduce MPLS facilities on a subset of the core nodes in order to exploit packet switching flexibility and multiplexing, thus providing induction of a better bandwidth allocation. In this article, we address this complex decisional problem with the support of a mathematical programming approach. We consider two-layer networks where MPLS is overlaid on top of transport networks-synchronous digital hierarchy (SDH) or wavelength division multiplexing (WDM)-depending on the required link speed. The discussions' decisions take into account the trade-off between the cost of adding MPLS support in the core nodes and the savings in the link bandwidth allocation due to the statistical multiplexing and the traffic grooming effects induced by MPLS nodes. The traffic matrix specifies for each point-to-point request a pair of values: a mean traffic value and an additional one. Using this traffic model, the effect of statistical multiplexing on a link allows the allocation of a capacity equal to the sum of all the mean values of the traffic demands routed on the link and only the highest additional one. The proposed approach is suitable to solve real instances in reasonable time.

Observations of highway traffic noise measurements behind barriers and comparisons to FHWA's Traffic Noise Model

DOT National Transportation Integrated Search

2001-08-20

In 1998, the United States Federal Highway Administration (FHWA) released a new tool for highway traffic noise prediction and noise barrier design, the Traffic Noise Model (TNM). In order to assess the accuracy and make recommendations on the use of ...

Spatial Copula Model for Imputing Traffic Flow Data from Remote Microwave Sensors.

PubMed

Ma, Xiaolei; Luan, Sen; Du, Bowen; Yu, Bin

2017-09-21

Issues of missing data have become increasingly serious with the rapid increase in usage of traffic sensors. Analyses of the Beijing ring expressway have showed that up to 50% of microwave sensors pose missing values. The imputation of missing traffic data must be urgently solved although a precise solution that cannot be easily achieved due to the significant number of missing portions. In this study, copula-based models are proposed for the spatial interpolation of traffic flow from remote traffic microwave sensors. Most existing interpolation methods only rely on covariance functions to depict spatial correlation and are unsuitable for coping with anomalies due to Gaussian consumption. Copula theory overcomes this issue and provides a connection between the correlation function and the marginal distribution function of traffic flow. To validate copula-based models, a comparison with three kriging methods is conducted. Results indicate that copula-based models outperform kriging methods, especially on roads with irregular traffic patterns. Copula-based models demonstrate significant potential to impute missing data in large-scale transportation networks.

Aircraft/Air Traffic Management Functional Analysis Model. Version 2.0; User's Guide

NASA Technical Reports Server (NTRS)

Etheridge, Melvin; Plugge, Joana; Retina, Nusrat

1998-01-01

The Aircraft/Air Traffic Management Functional Analysis Model, Version 2.0 (FAM 2.0), is a discrete event simulation model designed to support analysis of alternative concepts in air traffic management and control. FAM 2.0 was developed by the Logistics Management Institute (LMI) a National Aeronautics and Space Administration (NASA) contract. This document provides a guide for using the model in analysis. Those interested in making enhancements or modification to the model should consult the companion document, Aircraft/Air Traffic Management Functional Analysis Model, Version 2.0 Technical Description.

Cellular automata model for use with real freeway data

DOT National Transportation Integrated Search

2002-01-01

The exponential rate of increase in freeway traffic is expanding the need for accurate and : realistic methods to model and predict traffic flow. Traffic modeling and simulation facilitates an : examination of both microscopic and macroscopic views o...

Neural networks for continuous online learning and control.

PubMed

Choy, Min Chee; Srinivasan, Dipti; Cheu, Ruey Long

2006-11-01

This paper proposes a new hybrid neural network (NN) model that employs a multistage online learning process to solve the distributed control problem with an infinite horizon. Various techniques such as reinforcement learning and evolutionary algorithm are used to design the multistage online learning process. For this paper, the infinite horizon distributed control problem is implemented in the form of real-time distributed traffic signal control for intersections in a large-scale traffic network. The hybrid neural network model is used to design each of the local traffic signal controllers at the respective intersections. As the state of the traffic network changes due to random fluctuation of traffic volumes, the NN-based local controllers will need to adapt to the changing dynamics in order to provide effective traffic signal control and to prevent the traffic network from becoming overcongested. Such a problem is especially challenging if the local controllers are used for an infinite horizon problem where online learning has to take place continuously once the controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District (CBD) of Singapore has been developed using PARAMICS microscopic simulation program. As the complexity of the simulation increases, results show that the hybrid NN model provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as a new, continuously updated simultaneous perturbation stochastic approximation-based neural network (SPSA-NN). Using the hybrid NN model, the total mean delay of each vehicle has been reduced by 78% and the total mean stoppage time of each vehicle has been reduced by 84% compared to the existing traffic signal control algorithm. This shows the efficacy of the hybrid NN model in solving large-scale traffic signal control problem in a distributed manner. Also, it indicates the possibility of using the hybrid NN model for other applications that are similar in nature as the infinite horizon distributed control problem.

Applicability of models to estimate traffic noise for urban roads.

PubMed

Melo, Ricardo A; Pimentel, Roberto L; Lacerda, Diego M; Silva, Wekisley M

2015-01-01

Traffic noise is a highly relevant environmental impact in cities. Models to estimate traffic noise, in turn, can be useful tools to guide mitigation measures. In this paper, the applicability of models to estimate noise levels produced by a continuous flow of vehicles on urban roads is investigated. The aim is to identify which models are more appropriate to estimate traffic noise in urban areas since several models available were conceived to estimate noise from highway traffic. First, measurements of traffic noise, vehicle count and speed were carried out in five arterial urban roads of a brazilian city. Together with geometric measurements of width of lanes and distance from noise meter to lanes, these data were input in several models to estimate traffic noise. The predicted noise levels were then compared to the respective measured counterparts for each road investigated. In addition, a chart showing mean differences in noise between estimations and measurements is presented, to evaluate the overall performance of the models. Measured Leq values varied from 69 to 79 dB(A) for traffic flows varying from 1618 to 5220 vehicles/h. Mean noise level differences between estimations and measurements for all urban roads investigated ranged from -3.5 to 5.5 dB(A). According to the results, deficiencies of some models are discussed while other models are identified as applicable to noise estimations on urban roads in a condition of continuous flow. Key issues to apply such models to urban roads are highlighted.

Comparison of modeled traffic exposure zones using on-road air pollution measurements

EPA Science Inventory

Modeled traffic data were used to develop traffic exposure zones (TEZs) such as traffic delay, high volume, and transit routes in the Research Triangle area of North Carolina (USA). On-road air pollution measurements of nitrogen dioxide (NO2), carbon monoxide (CO), carbon dioxid...

Linking Traffic Noise, Noise Annoyance and Life Satisfaction: A Case Study

PubMed Central

Urban, Jan; Máca, Vojtěch

2013-01-01

The primary purpose of this study was to explore the link between rail and road traffic noise and overall life satisfaction. While the negative relationship between residential satisfaction and traffic noise is relatively well-established, much less is known about the effect of traffic noise on overall life satisfaction. Based on results of previous studies, we propose a model that links objective noise levels, noise sensitivity, noise annoyance, residential satisfaction and life satisfaction. Since it is not clear whether a bottom-up or top-down relationship between residential satisfaction and life satisfaction holds, we specify models that incorporate both of these theoretical propositions. Empirical models are tested using structural equation modeling and data from a survey among residents of areas with high levels of road traffic noise (n1 = 354) and rail traffic noise (n2 = 228). We find that traffic noise has a negative effect on residential satisfaction, but no significant direct or indirect effects on overall life satisfaction. Noise annoyance due to road and rail traffic noise has strong negative effect on residential satisfaction rather than on overall life satisfaction. These results are very similar for the road and railway traffic contexts and regardless of whether the model assumes the top-down or bottom-up direction of the causation between life satisfaction and residential satisfaction. PMID:23652784

Linking traffic noise, noise annoyance and life satisfaction: a case study.

PubMed

Urban, Jan; Máca, Vojtěch

2013-05-07

The primary purpose of this study was to explore the link between rail and road traffic noise and overall life satisfaction. While the negative relationship between residential satisfaction and traffic noise is relatively well-established, much less is known about the effect of traffic noise on overall life satisfaction. Based on results of previous studies, we propose a model that links objective noise levels, noise sensitivity, noise annoyance, residential satisfaction and life satisfaction. Since it is not clear whether a bottom-up or top-down relationship between residential satisfaction and life satisfaction holds, we specify models that incorporate both of these theoretical propositions. Empirical models are tested using structural equation modeling and data from a survey among residents of areas with high levels of road traffic noise (n1 = 354) and rail traffic noise (n2 = 228). We find that traffic noise has a negative effect on residential satisfaction, but no significant direct or indirect effects on overall life satisfaction. Noise annoyance due to road and rail traffic noise has strong negative effect on residential satisfaction rather than on overall life satisfaction. These results are very similar for the road and railway traffic contexts and regardless of whether the model assumes the top-down or bottom-up direction of the causation between life satisfaction and residential satisfaction.

Human Brain Modeling with Its Anatomical Structure and Realistic Material Properties for Brain Injury Prediction.

PubMed

Atsumi, Noritoshi; Nakahira, Yuko; Tanaka, Eiichi; Iwamoto, Masami

2018-05-01

Impairments of executive brain function after traumatic brain injury (TBI) due to head impacts in traffic accidents need to be obviated. Finite element (FE) analyses with a human brain model facilitate understanding of the TBI mechanisms. However, conventional brain FE models do not suitably describe the anatomical structure in the deep brain, which is a critical region for executive brain function, and the material properties of brain parenchyma. In this study, for better TBI prediction, a novel brain FE model with anatomical structure in the deep brain was developed. The developed model comprises a constitutive model of brain parenchyma considering anisotropy and strain rate dependency. Validation was performed against postmortem human subject test data associated with brain deformation during head impact. Brain injury analyses were performed using head acceleration curves obtained from reconstruction analysis of rear-end collision with a human whole-body FE model. The difference in structure was found to affect the regions of strain concentration, while the difference in material model contributed to the peak strain value. The injury prediction result by the proposed model was consistent with the characteristics in the neuroimaging data of TBI patients due to traffic accidents.

Joint impacts of minimum legal drinking age and beer taxes on US youth traffic fatalities, 1975 to 2001.

PubMed

Ponicki, William R; Gruenewald, Paul J; LaScala, Elizabeth A

2007-05-01

There is a considerable body of prior research indicating that a number of public policies that limit alcohol availability affect youth traffic fatalities. These limitations can be economic (e.g., beverage taxation), physical (e.g., numbers or operating hours of alcohol outlets), or demographic (e.g., minimum legal drinking age). The estimated impacts of these policies differ widely across studies. A full-price theoretical approach suggests that people weigh the benefits of drinking against the sum of all the associated costs, including the price of the beverages themselves plus the difficulty of obtaining them and any additional risks of injury or punishment related to their use. This study tested one prediction of this model, namely that the impact from changing one availability-related cost depends on the level of other components of full cost. The current analyses concentrate on 2 forms of limitations on availability that have been shown to affect youth traffic fatalities: minimum legal drinking age (MLDA) laws and beer taxes. The interdependence between the impacts of MLDA and taxes is investigated using a panel of 48 US states over the period 1975 to 2001. All age-group-specific models control for numerous other variables previously shown to affect vehicle fatalities, as well as fixed effects to account for unexplained crosssectional and time-series variation. The analyses showed that raising either MLDA or beer taxes in isolation led to fewer youth traffic fatalities. As expected, a given change in MLDA causes a larger proportional change in fatalities when beer taxes are low than when they are high. These findings suggest that a community's expected benefit from a proposed limitation on alcohol availability depends on its current regulatory environment. Specifically, communities with relatively strong existing policies might expect smaller impacts than suggested by prior research, while places with weak current regulations might expect larger benefits from the same policy initiative.

Land use and traffic congestion.

DOT National Transportation Integrated Search

2012-03-01

"The study investigated the link between land use, travel behavior, and traffic congestion. Popular wisdom : suggests that higher-density development patterns may be beneficial in reducing private vehicle dependency : and use, which if true, could ho...

Curve Estimation of Number of People Killed in Traffic Accidents in Turkey

NASA Astrophysics Data System (ADS)

Berkhan Akalin, Kadir; Karacasu, Murat; Altin, Arzu Yavuz; Ergül, Bariş

2016-10-01

One or more than one vehicle in motion on the highway involving death, injury and loss events which have resulted are called accidents. As a result of increasing population and traffic density, traffic accidents continue to increase and this leads to both human losses and harm to the economy. In addition, also leads to social problems. As a result of increasing population and traffic density, traffic accidents continue to increase and this leads to both human losses and harm to the economy. In addition to this, it also leads to social problems. As a result of traffic accidents, millions of people die year by year. A great majority of these accidents occur in developing countries. One of the most important tasks of transportation engineers is to reduce traffic accidents by creating a specific system. For that reason, statistical information about traffic accidents which occur in the past years should be organized by versed people. Factors affecting the traffic accidents are analyzed in various ways. In this study, modelling the number of people killed in traffic accidents in Turkey is determined. The dead people were modelled using curve fitting method with the number of people killed in traffic accidents in Turkey dataset between 1990 and 2014. It was also predicted the number of dead people by using various models for the future. It is decided that linear model is suitable for the estimates.

Traffic Signal Synchronization in the Saturated High-Density Grid Road Network

PubMed Central

Hu, Xiaojian; Lu, Jian; Wang, Wei; Zhirui, Ye

2015-01-01

Most existing traffic signal synchronization strategies do not perform well in the saturated high-density grid road network (HGRN). Traffic congestion often occurs in the saturated HGRN, and the mobility of the network is difficult to restore. In order to alleviate traffic congestion and to improve traffic efficiency in the network, the study proposes a regional traffic signal synchronization strategy, named the long green and long red (LGLR) traffic signal synchronization strategy. The essence of the strategy is to control the formation and dissipation of queues and to maximize the efficiency of traffic flows at signalized intersections in the saturated HGRN. With this strategy, the same signal control timing plan is used at all signalized intersections in the HGRN, and the straight phase of the control timing plan has a long green time and a long red time. Therefore, continuous traffic flows can be maintained when vehicles travel, and traffic congestion can be alleviated when vehicles stop. Using the strategy, the LGLR traffic signal synchronization model is developed, with the objective of minimizing the number of stops. Finally, the simulation is executed to analyze the performance of the model by comparing it to other models, and the superiority of the LGLR model is evident in terms of delay, number of stops, queue length, and overall performance in the saturated HGRN. PMID:25663835

Multilayer Statistical Intrusion Detection in Wireless Networks

NASA Astrophysics Data System (ADS)

Hamdi, Mohamed; Meddeb-Makhlouf, Amel; Boudriga, Noureddine

2008-12-01

The rapid proliferation of mobile applications and services has introduced new vulnerabilities that do not exist in fixed wired networks. Traditional security mechanisms, such as access control and encryption, turn out to be inefficient in modern wireless networks. Given the shortcomings of the protection mechanisms, an important research focuses in intrusion detection systems (IDSs). This paper proposes a multilayer statistical intrusion detection framework for wireless networks. The architecture is adequate to wireless networks because the underlying detection models rely on radio parameters and traffic models. Accurate correlation between radio and traffic anomalies allows enhancing the efficiency of the IDS. A radio signal fingerprinting technique based on the maximal overlap discrete wavelet transform (MODWT) is developed. Moreover, a geometric clustering algorithm is presented. Depending on the characteristics of the fingerprinting technique, the clustering algorithm permits to control the false positive and false negative rates. Finally, simulation experiments have been carried out to validate the proposed IDS.

Can Simulator Immersion Change Cognitive Style? Results from a Cross-Sectional Study of Field-Dependence--Independence in Air Traffic Control Students

ERIC Educational Resources Information Center

Van Eck, Richard N.; Fu, Hongxia; Drechsel, Paul V. J.

2015-01-01

Air traffic control (ATC) operations are critical to the U.S. aviation infrastructure, making ATC training a critical area of study. Because ATC performance is heavily dependent on visual processing, it is important to understand how to screen for or promote relevant visual processing abilities. While conventional wisdom has maintained that such…

Measurement-based auralization methodology for the assessment of noise mitigation measures

NASA Astrophysics Data System (ADS)

Thomas, Pieter; Wei, Weigang; Van Renterghem, Timothy; Botteldooren, Dick

2016-09-01

The effect of noise mitigation measures is generally expressed by noise levels only, neglecting the listener's perception. In this study, an auralization methodology is proposed that enables an auditive preview of noise abatement measures for road traffic noise, based on the direction dependent attenuation of a priori recordings made with a dedicated 32-channel spherical microphone array. This measurement-based auralization has the advantage that all non-road traffic sounds that create the listening context are present. The potential of this auralization methodology is evaluated through the assessment of the effect of an L-shaped mound. The angular insertion loss of the mound is estimated by using the ISO 9613-2 propagation model, the Pierce barrier diffraction model and the Harmonoise point-to-point model. The realism of the auralization technique is evaluated by listening tests, indicating that listeners had great difficulty in differentiating between a posteriori recordings and auralized samples, which shows the validity of the followed approaches.

Sampling, testing and modeling particle size distribution in urban catch basins.

PubMed

Garofalo, G; Carbone, M; Piro, P

2014-01-01

The study analyzed the particle size distribution of particulate matter (PM) retained in two catch basins located, respectively, near a parking lot and a traffic intersection with common high levels of traffic activity. Also, the treatment performance of a filter medium was evaluated by laboratory testing. The experimental treatment results and the field data were then used as inputs to a numerical model which described on a qualitative basis the hydrological response of the two catchments draining into each catch basin, respectively, and the quality of treatment provided by the filter during the measured rainfall. The results show that PM concentrations were on average around 300 mg/L (parking lot site) and 400 mg/L (road site) for the 10 rainfall-runoff events observed. PM with a particle diameter of <45 μm represented 40-50% of the total PM mass. The numerical model showed that a catch basin with a filter unit can remove 30 to 40% of the PM load depending on the storm characteristics.

Record length requirement of long-range dependent teletraffic

NASA Astrophysics Data System (ADS)

Li, Ming

2017-04-01

This article contributes the highlights mainly in two folds. On the one hand, it presents a formula to compute the upper bound of the variance of the correlation periodogram measurement of teletraffic (traffic for short) with long-range dependence (LRD) for a given record length T and a given value of the Hurst parameter H (Theorems 1 and 2). On the other hand, it proposes two formulas for the computation of the variance upper bound of the correlation periodogram measurement of traffic of fractional Gaussian noise (fGn) type and the generalized Cauchy (GC) type, respectively (Corollaries 1 and 2). They may constitute a reference guideline of record length requirement of traffic with LRD. In addition, record length requirement for the correlation periodogram measurement of traffic with either the Schuster type or the Bartlett one is studied and the present results about it show that both types of periodograms may be used for the correlation measurement of traffic with a pre-desired variance bound of correlation estimation. Moreover, real traffic in the Internet Archive by the Special Interest Group on Data Communication under the Association for Computing Machinery of US (ACM SIGCOMM) is analyzed in the case study in this topic.

Drawing for Traffic Marking Using Bidirectional Gradient-Based Detection with MMS LIDAR Intensity

NASA Astrophysics Data System (ADS)

Takahashi, G.; Takeda, H.; Nakamura, K.

2016-06-01

Recently, the development of autonomous cars is accelerating on the integration of highly advanced artificial intelligence, which increases demand for a digital map with high accuracy. In particular, traffic markings are required to be precisely digitized since automatic driving utilizes them for position detection. To draw traffic markings, we benefit from Mobile Mapping Systems (MMS) equipped with high-density Laser imaging Detection and Ranging (LiDAR) scanners, which produces large amount of data efficiently with XYZ coordination along with reflectance intensity. Digitizing this data, on the other hand, conventionally has been dependent on human operation, which thus suffers from human errors, subjectivity errors, and low reproductivity. We have tackled this problem by means of automatic extraction of traffic marking, which partially accomplished to draw several traffic markings (G. Takahashi et al., 2014). The key idea of the method was extracting lines using the Hough transform strategically focused on changes in local reflection intensity along scan lines. However, it failed to extract traffic markings properly in a densely marked area, especially when local changing points are close each other. In this paper, we propose a bidirectional gradient-based detection method where local changing points are labelled with plus or minus group. Given that each label corresponds to the boundary between traffic markings and background, we can identify traffic markings explicitly, meaning traffic lines are differentiated correctly by the proposed method. As such, our automated method, a highly accurate and non-human-operator-dependent method using bidirectional gradient-based algorithm, can successfully extract traffic lines composed of complex shapes such as a cross walk, resulting in minimizing cost and obtaining highly accurate results.

FHWA Traffic Noise Model user's guide (version 2.5 addendum)

DOT National Transportation Integrated Search

2004-04-30

In March 1998, the Federal Highway Administration (FHWA), Office of Natural and Human Environment, released the FHWA Traffic Noise Model (TNM), Version 1.0, a state-of-the-art computer model for highway traffic noise prediction and analysis. Since th...

Impact of Rainfall on Multilane Roundabout Flowrate Contraction

NASA Astrophysics Data System (ADS)

PARKSHIR, Amir; BEN-EDIGBE, Johnnie

2017-08-01

In this study, roundabouts at two sites in the Malaysia were investigated under rainy and dry weather conditions. Two automatic traffic counters per roundabout arm as well as two rain gauge stations were used to collect data at each surveyed site. Nearly one million vehicles were investigated at four sites. Vehicle volume, speeds and headways for entry and circulating flows were collected continuously at each roundabout about arm for six weeks between November 2013 and January 2014. Empirical regression technique and gap-acceptance models were modified and used to analyze roundabout capacity. Good fits to the data were obtained; the results also fit models developed in other countries. It was assumed that entry capacity depends on the geometric characteristics of the roundabout, particularly the diameter of the outside circle of the intersection. It was also postulated that geometric characteristics determine the speed of vehicles around the central island and, therefore, have an impact on the gap-acceptance process and consequently the capacity. Only off-peak traffic data per light, moderate or heavy rainfall were analysed. Peak traffic data were not used because of the presence of peak traffic flow. Passenger car equivalent values being an instrument of conversion from traffic volume to flow were modified. Results show that, average entry capacity loss is about 22.6% under light rainfall, about 18.1% under moderate rainfall and about 5.6% under heavy rainfall. Significant entry capacity loss would result from rainfall irrespective of their intensity. It can be postulated that entry capacity loss under heavy rainfall is lowest because the advantage enjoyed by circulating flow would be greatly reduced with increased rainfall intensity. The paper concluded that rainfall has significant impact of flowrate contraction at roundabouts.

An extended car-following model considering the acceleration derivative in some typical traffic environments

NASA Astrophysics Data System (ADS)

Zhou, Tong; Chen, Dong; Liu, Weining

2018-03-01

Based on the full velocity difference and acceleration car-following model, an extended car-following model is proposed by considering the vehicle’s acceleration derivative. The stability condition is given by applying the control theory. Considering some typical traffic environments, the results of theoretical analysis and numerical simulation show the extended model has a more actual acceleration of string vehicles than that of the previous models in starting process, stopping process and sudden brake. Meanwhile, the traffic jams more easily occur when the coefficient of vehicle’s acceleration derivative increases, which is presented by space-time evolution. The results confirm that the vehicle’s acceleration derivative plays an important role in the traffic jamming transition and the evolution of traffic congestion.

Using Inspiration from Synaptic Plasticity Rules to Optimize Traffic Flow in Distributed Engineered Networks.

PubMed

Suen, Jonathan Y; Navlakha, Saket

2017-05-01

Controlling the flow and routing of data is a fundamental problem in many distributed networks, including transportation systems, integrated circuits, and the Internet. In the brain, synaptic plasticity rules have been discovered that regulate network activity in response to environmental inputs, which enable circuits to be stable yet flexible. Here, we develop a new neuro-inspired model for network flow control that depends only on modifying edge weights in an activity-dependent manner. We show how two fundamental plasticity rules, long-term potentiation and long-term depression, can be cast as a distributed gradient descent algorithm for regulating traffic flow in engineered networks. We then characterize, both by simulation and analytically, how different forms of edge-weight-update rules affect network routing efficiency and robustness. We find a close correspondence between certain classes of synaptic weight update rules derived experimentally in the brain and rules commonly used in engineering, suggesting common principles to both.

A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine.

PubMed

Shang, Qiang; Lin, Ciyun; Yang, Zhaosheng; Bing, Qichun; Zhou, Xiyang

2016-01-01

Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust.

A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine

PubMed Central

Lin, Ciyun; Yang, Zhaosheng; Bing, Qichun; Zhou, Xiyang

2016-01-01

Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust. PMID:27551829

Macroscopic modeling of freeway traffic using an artificial neural network

DOT National Transportation Integrated Search

1997-01-01

Traffic flow on freeways is a complex process that often is described by a set of highly nonlinear, dynamic equations in the form of a macroscopic traffic flow model. However, some of the existing macroscopic models have been found to exhibit instabi...

Modeling Traffic on the Web Graph

NASA Astrophysics Data System (ADS)

Meiss, Mark R.; Gonçalves, Bruno; Ramasco, José J.; Flammini, Alessandro; Menczer, Filippo

Analysis of aggregate and individual Web requests shows that PageRank is a poor predictor of traffic. We use empirical data to characterize properties of Web traffic not reproduced by Markovian models, including both aggregate statistics such as page and link traffic, and individual statistics such as entropy and session size. As no current model reconciles all of these observations, we present an agent-based model that explains them through realistic browsing behaviors: (1) revisiting bookmarked pages; (2) backtracking; and (3) seeking out novel pages of topical interest. The resulting model can reproduce the behaviors we observe in empirical data, especially heterogeneous session lengths, reconciling the narrowly focused browsing patterns of individual users with the extreme variance in aggregate traffic measurements. We can thereby identify a few salient features that are necessary and sufficient to interpret Web traffic data. Beyond the descriptive and explanatory power of our model, these results may lead to improvements in Web applications such as search and crawling.

Differential Effects of Roads and Traffic on Space Use and Movements of Native Forest-Dependent and Introduced Edge-Tolerant Species

PubMed Central

Chen, Hsiang Ling; Koprowski, John L.

2016-01-01

Anthropogenic infrastructure such as roads and non-native species are major causes of species endangerment. Understanding animal behavioral responses to roads and traffic provides insight into causes and mechanisms of effects of linear development on wildlife and aids effective mitigation and conservation. We investigated effects of roads and traffic on space use and movements of two forest-dwelling species: endemic, forest-dependent Mount Graham red squirrels (Tamiasciurus hudsonicus grahamensis) and introduced, edge-tolerant Abert’s squirrels (Sciurus aberti). To assess the effects of roads on space use and movement patterns, we compared the probability that a squirrel home range included roads and random lines in forests, and assessed effects of traffic intensity on rate of road crossing and movement patterns. Red squirrels avoided areas adjacent to roads and rarely crossed roads. In contrast, Abert’s squirrels were more likely to include roads in their home ranges compared to random lines in forests. Both red squirrels and Abert’s squirrels increased speed when crossing roads, compared to before and after road crossings. Increased hourly traffic volume reduced the rate of road crossings by both species. Behavioral responses of red squirrels to roads and traffic resemble responses to elevated predation risk, including reduced speed near roads and increased tortuosity of movement paths with increased traffic volume. In contrast, Abert’s squirrels appeared little affected by roads and traffic with tortuosity of movement paths reduced as distance to roads decreased. We found that species with similar body size category (<1 kg) but different habitat preference and foraging strategy responded to roads differently and demonstrated that behavior and ecology are important when considering effects of roads on wildlife. Our results indicate that roads restricted movements and space use of a native forest-dependent species while creating habitat preferred by an introduced, edge-tolerant species. PMID:26821366

Differential Effects of Roads and Traffic on Space Use and Movements of Native Forest-Dependent and Introduced Edge-Tolerant Species.

PubMed

Chen, Hsiang Ling; Koprowski, John L

2016-01-01

Anthropogenic infrastructure such as roads and non-native species are major causes of species endangerment. Understanding animal behavioral responses to roads and traffic provides insight into causes and mechanisms of effects of linear development on wildlife and aids effective mitigation and conservation. We investigated effects of roads and traffic on space use and movements of two forest-dwelling species: endemic, forest-dependent Mount Graham red squirrels (Tamiasciurus hudsonicus grahamensis) and introduced, edge-tolerant Abert's squirrels (Sciurus aberti). To assess the effects of roads on space use and movement patterns, we compared the probability that a squirrel home range included roads and random lines in forests, and assessed effects of traffic intensity on rate of road crossing and movement patterns. Red squirrels avoided areas adjacent to roads and rarely crossed roads. In contrast, Abert's squirrels were more likely to include roads in their home ranges compared to random lines in forests. Both red squirrels and Abert's squirrels increased speed when crossing roads, compared to before and after road crossings. Increased hourly traffic volume reduced the rate of road crossings by both species. Behavioral responses of red squirrels to roads and traffic resemble responses to elevated predation risk, including reduced speed near roads and increased tortuosity of movement paths with increased traffic volume. In contrast, Abert's squirrels appeared little affected by roads and traffic with tortuosity of movement paths reduced as distance to roads decreased. We found that species with similar body size category (<1 kg) but different habitat preference and foraging strategy responded to roads differently and demonstrated that behavior and ecology are important when considering effects of roads on wildlife. Our results indicate that roads restricted movements and space use of a native forest-dependent species while creating habitat preferred by an introduced, edge-tolerant species.

Microscopic modeling of multi-lane highway traffic flow

NASA Astrophysics Data System (ADS)

Hodas, Nathan O.; Jagota, Anand

2003-12-01

We discuss a microscopic model for the study of multi-lane highway traffic flow dynamics. Each car experiences a force resulting from a combination of the desire of the driver to attain a certain velocity, aerodynamic drag, and change of the force due to car-car interactions. The model also includes multi-lane simulation capability and the ability to add and remove obstructions. We implement the model via a Java applet, which is used to simulate traffic jam formation, the effect of bottlenecks on traffic flow, and the existence of light, medium, and heavy traffic flow. The simulations also provide insight into how the properties of individual cars result in macroscopic behavior. Because the investigation of emergent characteristics is so common in physics, the study of traffic in this manner sheds new light on how the micro-to-macro transition works in general.

Variable speed limit strategies analysis with link transmission model on urban expressway

NASA Astrophysics Data System (ADS)

Li, Shubin; Cao, Danni

2018-02-01

The variable speed limit (VSL) is a kind of active traffic management method. Most of the strategies are used in the expressway traffic flow control in order to ensure traffic safety. However, the urban expressway system is the main artery, carrying most traffic pressure. It has similar traffic characteristics with the expressways between cities. In this paper, the improved link transmission model (LTM) combined with VSL strategies is proposed, based on the urban expressway network. The model can simulate the movement of the vehicles and the shock wave, and well balance the relationship between the amount of calculation and accuracy. Furthermore, the optimal VSL strategy can be proposed based on the simulation method. It can provide management strategies for managers. Finally, a simple example is given to illustrate the model and method. The selected indexes are the average density, the average speed and the average flow on the traffic network in the simulation. The simulation results show that the proposed model and method are feasible. The VSL strategy can effectively alleviate traffic congestion in some cases, and greatly promote the efficiency of the transportation system.

Spatial Copula Model for Imputing Traffic Flow Data from Remote Microwave Sensors

PubMed Central

Ma, Xiaolei; Du, Bowen; Yu, Bin

2017-01-01

Issues of missing data have become increasingly serious with the rapid increase in usage of traffic sensors. Analyses of the Beijing ring expressway have showed that up to 50% of microwave sensors pose missing values. The imputation of missing traffic data must be urgently solved although a precise solution that cannot be easily achieved due to the significant number of missing portions. In this study, copula-based models are proposed for the spatial interpolation of traffic flow from remote traffic microwave sensors. Most existing interpolation methods only rely on covariance functions to depict spatial correlation and are unsuitable for coping with anomalies due to Gaussian consumption. Copula theory overcomes this issue and provides a connection between the correlation function and the marginal distribution function of traffic flow. To validate copula-based models, a comparison with three kriging methods is conducted. Results indicate that copula-based models outperform kriging methods, especially on roads with irregular traffic patterns. Copula-based models demonstrate significant potential to impute missing data in large-scale transportation networks. PMID:28934164

A knowledge-based system for controlling automobile traffic

NASA Technical Reports Server (NTRS)

Maravas, Alexander; Stengel, Robert F.

1994-01-01

Transportation network capacity variations arising from accidents, roadway maintenance activity, and special events as well as fluctuations in commuters' travel demands complicate traffic management. Artificial intelligence concepts and expert systems can be useful in framing policies for incident detection, congestion anticipation, and optimal traffic management. This paper examines the applicability of intelligent route guidance and control as decision aids for traffic management. Basic requirements for managing traffic are reviewed, concepts for studying traffic flow are introduced, and mathematical models for modeling traffic flow are examined. Measures for quantifying transportation network performance levels are chosen, and surveillance and control strategies are evaluated. It can be concluded that automated decision support holds great promise for aiding the efficient flow of automobile traffic over limited-access roadways, bridges, and tunnels.

Road traffic impact on urban water quality: a step towards integrated traffic, air and stormwater modelling.

PubMed

Fallah Shorshani, Masoud; Bonhomme, Céline; Petrucci, Guido; André, Michel; Seigneur, Christian

2014-04-01

Methods for simulating air pollution due to road traffic and the associated effects on stormwater runoff quality in an urban environment are examined with particular emphasis on the integration of the various simulation models into a consistent modelling chain. To that end, the models for traffic, pollutant emissions, atmospheric dispersion and deposition, and stormwater contamination are reviewed. The present study focuses on the implementation of a modelling chain for an actual urban case study, which is the contamination of water runoff by cadmium (Cd), lead (Pb), and zinc (Zn) in the Grigny urban catchment near Paris, France. First, traffic emissions are calculated with traffic inputs using the COPERT4 methodology. Next, the atmospheric dispersion of pollutants is simulated with the Polyphemus line source model and pollutant deposition fluxes in different subcatchment areas are calculated. Finally, the SWMM water quantity and quality model is used to estimate the concentrations of pollutants in stormwater runoff. The simulation results are compared to mass flow rates and concentrations of Cd, Pb and Zn measured at the catchment outlet. The contribution of local traffic to stormwater contamination is estimated to be significant for Pb and, to a lesser extent, for Zn and Cd; however, Pb is most likely overestimated due to outdated emissions factors. The results demonstrate the importance of treating distributed traffic emissions from major roadways explicitly since the impact of these sources on concentrations in the catchment outlet is underestimated when those traffic emissions are spatially averaged over the catchment area.

Evaluation of Intersection Traffic Control Measures through Simulation

NASA Astrophysics Data System (ADS)

Asaithambi, Gowri; Sivanandan, R.

2015-12-01

Modeling traffic flow is stochastic in nature due to randomness in variables such as vehicle arrivals and speeds. Due to this and due to complex vehicular interactions and their manoeuvres, it is extremely difficult to model the traffic flow through analytical methods. To study this type of complex traffic system and vehicle interactions, simulation is considered as an effective tool. Application of homogeneous traffic models to heterogeneous traffic may not be able to capture the complex manoeuvres and interactions in such flows. Hence, a microscopic simulation model for heterogeneous traffic is developed using object oriented concepts. This simulation model acts as a tool for evaluating various control measures at signalized intersections. The present study focuses on the evaluation of Right Turn Lane (RTL) and Channelised Left Turn Lane (CLTL). A sensitivity analysis was performed to evaluate RTL and CLTL by varying the approach volumes, turn proportions and turn lane lengths. RTL is found to be advantageous only up to certain approach volumes and right-turn proportions, beyond which it is counter-productive. CLTL is found to be advantageous for lower approach volumes for all turn proportions, signifying the benefits of CLTL. It is counter-productive for higher approach volume and lower turn proportions. This study pinpoints the break-even points for various scenarios. The developed simulation model can be used as an appropriate intersection lane control tool for enhancing the efficiency of flow at intersections. This model can also be employed for scenario analysis and can be valuable to field traffic engineers in implementing vehicle-type based and lane-based traffic control measures.

A hazard-based duration model for analyzing crossing behavior of cyclists and electric bike riders at signalized intersections.

PubMed

Yang, Xiaobao; Huan, Mei; Abdel-Aty, Mohamed; Peng, Yichuan; Gao, Ziyou

2015-01-01

This paper presents a hazard-based duration approach to investigate riders' waiting times, violation hazards, associated risk factors, and their differences between cyclists and electric bike riders at signalized intersections. A total of 2322 two-wheeled riders approaching the intersections during red light periods were observed in Beijing, China. The data were classified into censored and uncensored data to distinguish between safe crossing and red-light running behavior. The results indicated that the red-light crossing behavior of most riders was dependent on waiting time. They were inclined to terminate waiting behavior and run against the traffic light with the increase of waiting duration. Over half of the observed riders cannot endure 49s or longer. 25% of the riders can endure 97s or longer. Rider type, gender, waiting position, conformity tendency and crossing traffic volume were identified to have significant effects on riders' waiting times and violation hazards. Electric bike riders were found to be more sensitive to the external risk factors such as other riders' crossing behavior and crossing traffic volume than cyclists. Moreover, unobserved heterogeneity was examined in the proposed models. The finding of this paper can explain when and why cyclists and electric bike riders run against the red light at intersections. The results of this paper are useful for traffic design and management agencies to implement strategies to enhance the safety of riders. Copyright © 2014 Elsevier Ltd. All rights reserved.

Temporal variation of traffic on highways and the development of accurate temporal allocation factors for air pollution analyses

NASA Astrophysics Data System (ADS)

Batterman, Stuart; Cook, Richard; Justin, Thomas

2015-04-01

Traffic activity encompasses the number, mix, speed and acceleration of vehicles on roadways. The temporal pattern and variation of traffic activity reflects vehicle use, congestion and safety issues, and it represents a major influence on emissions and concentrations of traffic-related air pollutants. Accurate characterization of vehicle flows is critical in analyzing and modeling urban and local-scale pollutants, especially in near-road environments and traffic corridors. This study describes methods to improve the characterization of temporal variation of traffic activity. Annual, monthly, daily and hourly temporal allocation factors (TAFs), which describe the expected temporal variation in traffic activity, were developed using four years of hourly traffic activity data recorded at 14 continuous counting stations across the Detroit, Michigan, U.S. region. Five sites also provided vehicle classification. TAF-based models provide a simple means to apportion annual average estimates of traffic volume to hourly estimates. The analysis shows the need to separate TAFs for total and commercial vehicles, and weekdays, Saturdays, Sundays and observed holidays. Using either site-specific or urban-wide TAFs, nearly all of the variation in historical traffic activity at the street scale could be explained; unexplained variation was attributed to adverse weather, traffic accidents and construction. The methods and results presented in this paper can improve air quality dispersion modeling of mobile sources, and can be used to evaluate and model temporal variation in ambient air quality monitoring data and exposure estimates.

Temporal variation of traffic on highways and the development of accurate temporal allocation factors for air pollution analyses

PubMed Central

Batterman, Stuart; Cook, Richard; Justin, Thomas

2015-01-01

Traffic activity encompasses the number, mix, speed and acceleration of vehicles on roadways. The temporal pattern and variation of traffic activity reflects vehicle use, congestion and safety issues, and it represents a major influence on emissions and concentrations of traffic-related air pollutants. Accurate characterization of vehicle flows is critical in analyzing and modeling urban and local-scale pollutants, especially in near-road environments and traffic corridors. This study describes methods to improve the characterization of temporal variation of traffic activity. Annual, monthly, daily and hourly temporal allocation factors (TAFs), which describe the expected temporal variation in traffic activity, were developed using four years of hourly traffic activity data recorded at 14 continuous counting stations across the Detroit, Michigan, U.S. region. Five sites also provided vehicle classification. TAF-based models provide a simple means to apportion annual average estimates of traffic volume to hourly estimates. The analysis shows the need to separate TAFs for total and commercial vehicles, and weekdays, Saturdays, Sundays and observed holidays. Using either site-specific or urban-wide TAFs, nearly all of the variation in historical traffic activity at the street scale could be explained; unexplained variation was attributed to adverse weather, traffic accidents and construction. The methods and results presented in this paper can improve air quality dispersion modeling of mobile sources, and can be used to evaluate and model temporal variation in ambient air quality monitoring data and exposure estimates. PMID:25844042

Comparison of Predictive Modeling Methods of Aircraft Landing Speed

NASA Technical Reports Server (NTRS)

Diallo, Ousmane H.

2012-01-01

Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.

Aggregated GPS tracking of vehicles and its use as a proxy of traffic-related air pollution emissions

NASA Astrophysics Data System (ADS)

Chen, Shimon; Bekhor, Shlomo; Yuval; Broday, David M.

2016-10-01

Most air quality models use traffic-related variables as an input. Previous studies estimated nearby vehicular activity through sporadic traffic counts or via traffic assignment models. Both methods have previously produced poor or no data for nights, weekends and holidays. Emerging technologies allow the estimation of traffic through passive monitoring of location-aware devices. Examples of such devices are GPS transceivers installed in vehicles. In this work, we studied traffic volumes that were derived from such data. Additionally, we used these data for estimating ambient nitrogen dioxide concentrations, using a non-linear optimisation model that includes basic dispersion properties. The GPS-derived data show great potential for use as a proxy for pollutant emissions from motor-vehicles.

Cellular automata model for urban road traffic flow considering pedestrian crossing street

NASA Astrophysics Data System (ADS)

Zhao, Han-Tao; Yang, Shuo; Chen, Xiao-Xu

2016-11-01

In order to analyze the effect of pedestrians' crossing street on vehicle flows, we investigated traffic characteristics of vehicles and pedestrians. Based on that, rules of lane changing, acceleration, deceleration, randomization and update are modified. Then we established two urban two-lane cellular automata models of traffic flow, one of which is about sections with non-signalized crosswalk and the other is on uncontrolled sections with pedestrians crossing street at random. MATLAB is used for numerical simulation of the different traffic conditions; meanwhile space-time diagram and relational graphs of traffic flow parameters are generated and then comparatively analyzed. Simulation results indicate that when vehicle density is lower than around 25 vehs/(km lane), pedestrians have modest impact on traffic flow, whereas when vehicle density is higher than about 60 vehs/(km lane), traffic speed and volume will decrease significantly especially on sections with non-signal-controlled crosswalk. The results illustrate that the proposed models reconstruct the traffic flow's characteristic with the situation where there are pedestrians crossing and can provide some practical reference for urban traffic management.

A new cellular automaton for signal controlled traffic flow based on driving behaviors

NASA Astrophysics Data System (ADS)

Wang, Yang; Chen, Yan-Yan

2015-03-01

The complexity of signal controlled traffic largely stems from the various driving behaviors developed in response to the traffic signal. However, the existing models take a few driving behaviors into account and consequently the traffic dynamics has not been completely explored. Therefore, a new cellular automaton model, which incorporates the driving behaviors typically manifesting during the different stages when the vehicles are moving toward a traffic light, is proposed in this paper. Numerical simulations have demonstrated that the proposed model can produce the spontaneous traffic breakdown and the dissolution of the over-saturated traffic phenomena. Furthermore, the simulation results indicate that the slow-to-start behavior and the inch-forward behavior can foster the traffic breakdown. Particularly, it has been discovered that the over-saturated traffic can be revised to be an under-saturated state when the slow-down behavior is activated after the spontaneous breakdown. Finally, the contributions of the driving behaviors on the traffic breakdown have been examined. Project supported by the National Basic Research Program of China (Grand No. 2012CB723303) and the Beijing Committee of Science and Technology, China (Grand No. Z1211000003120100).

Self-Organized Transport System

DOT National Transportation Integrated Search

2009-09-28

This report presents the findings of the simulation model for a self-organized transport system where traffic lights communicate with neighboring traffic lights and make decisions locally to adapt to traffic conditions in real time. The model is insp...

Neuronal activity-dependent membrane traffic at the neuromuscular junction

PubMed Central

Miana-Mena, Francisco Javier; Roux, Sylvie; Benichou, Jean-Claude; Osta, Rosario; Brûlet, Philippe

2002-01-01

During development and also in adulthood, synaptic connections are modulated by neuronal activity. To follow such modifications in vivo, new genetic tools are designed. The nontoxic C-terminal fragment of tetanus toxin (TTC) fused to a reporter gene such as LacZ retains the retrograde and transsynaptic transport abilities of the holotoxin itself. In this work, the hybrid protein is injected intramuscularly to analyze in vivo the mechanisms of intracellular and transneuronal traffics at the neuromuscular junction (NMJ). Traffic on both sides of the synapse are strongly dependent on presynaptic neural cell activity. In muscle, a directional membrane traffic concentrates β-galactosidase-TTC hybrid protein into the NMJ postsynaptic side. In neurons, the probe is sorted across the cell to dendrites and subsequently to an interconnected neuron. Such fusion protein, sensitive to presynaptic neuronal activity, would be extremely useful to analyze morphological changes and plasticity at the NMJ. PMID:11880654

Advanced source apportionment of size-resolved trace elements at multiple sites in London during winter

DOE PAGES

Visser, S.; Slowik, Jay G.; Furger, M.; ...

2015-10-12

Here, trace element measurements in PM 10–2.5, PM 2.5–1.0 and PM 1.0–0.3 aerosol were performed with 2 h time resolution at kerbside, urban background and rural sites during the ClearfLo winter 2012 campaign in London. The environment-dependent variability of emissions was characterized using the Multilinear Engine implementation of the positive matrix factorization model, conducted on data sets comprising all three sites but segregated by size. Combining the sites enabled separation of sources with high temporal covariance but significant spatial variability. Separation of sizes improved source resolution by preventing sources occurring in only a single size fraction from having too smallmore » a contribution for the model to resolve. Anchor profiles were retrieved internally by analysing data subsets, and these profiles were used in the analyses of the complete data sets of all sites for enhanced source apportionment. A total of nine different factors were resolved (notable elements in brackets): in PM 10–2.5, brake wear (Cu, Zr, Sb, Ba), other traffic-related (Fe), resuspended dust (Si, Ca), sea/road salt (Cl), aged sea salt (Na, Mg) and industrial (Cr, Ni); in PM 2.5–1.0, brake wear, other traffic-related, resuspended dust, sea/road salt, aged sea salt and S-rich (S); and in PM 1.0–0.3, traffic-related (Fe, Cu, Zr, Sb, Ba), resuspended dust, sea/road salt, aged sea salt, reacted Cl (Cl), S-rich and solid fuel (K, Pb). Human activities enhance the kerb-to-rural concentration gradients of coarse aged sea salt, typically considered to have a natural source, by 1.7–2.2. These site-dependent concentration differences reflect the effect of local resuspension processes in London. The anthropogenically influenced factors traffic (brake wear and other traffic-related processes), dust and sea/road salt provide further kerb-to-rural concentration enhancements by direct source emissions by a factor of 3.5–12.7. The traffic and dust factors are mainly emitted in PM 10–2.5 and show strong diurnal variations with concentrations up to 4 times higher during rush hour than during night-time. Regionally influenced S-rich and solid fuel factors, occurring primarily in PM 1.0–0.3, have negligible resuspension influences, and concentrations are similar throughout the day and across the regions.« less

Advanced source apportionment of size-resolved trace elements at multiple sites in London during winter

DOE Office of Scientific and Technical Information (OSTI.GOV)

Visser, S.; Slowik, Jay G.; Furger, M.

Here, trace element measurements in PM 10–2.5, PM 2.5–1.0 and PM 1.0–0.3 aerosol were performed with 2 h time resolution at kerbside, urban background and rural sites during the ClearfLo winter 2012 campaign in London. The environment-dependent variability of emissions was characterized using the Multilinear Engine implementation of the positive matrix factorization model, conducted on data sets comprising all three sites but segregated by size. Combining the sites enabled separation of sources with high temporal covariance but significant spatial variability. Separation of sizes improved source resolution by preventing sources occurring in only a single size fraction from having too smallmore » a contribution for the model to resolve. Anchor profiles were retrieved internally by analysing data subsets, and these profiles were used in the analyses of the complete data sets of all sites for enhanced source apportionment. A total of nine different factors were resolved (notable elements in brackets): in PM 10–2.5, brake wear (Cu, Zr, Sb, Ba), other traffic-related (Fe), resuspended dust (Si, Ca), sea/road salt (Cl), aged sea salt (Na, Mg) and industrial (Cr, Ni); in PM 2.5–1.0, brake wear, other traffic-related, resuspended dust, sea/road salt, aged sea salt and S-rich (S); and in PM 1.0–0.3, traffic-related (Fe, Cu, Zr, Sb, Ba), resuspended dust, sea/road salt, aged sea salt, reacted Cl (Cl), S-rich and solid fuel (K, Pb). Human activities enhance the kerb-to-rural concentration gradients of coarse aged sea salt, typically considered to have a natural source, by 1.7–2.2. These site-dependent concentration differences reflect the effect of local resuspension processes in London. The anthropogenically influenced factors traffic (brake wear and other traffic-related processes), dust and sea/road salt provide further kerb-to-rural concentration enhancements by direct source emissions by a factor of 3.5–12.7. The traffic and dust factors are mainly emitted in PM 10–2.5 and show strong diurnal variations with concentrations up to 4 times higher during rush hour than during night-time. Regionally influenced S-rich and solid fuel factors, occurring primarily in PM 1.0–0.3, have negligible resuspension influences, and concentrations are similar throughout the day and across the regions.« less

Mean-field velocity difference model considering the average effect of multi-vehicle interaction

NASA Astrophysics Data System (ADS)

Guo, Yan; Xue, Yu; Shi, Yin; Wei, Fang-ping; Lü, Liang-zhong; He, Hong-di

2018-06-01

In this paper, a mean-field velocity difference model(MFVD) is proposed to describe the average effect of multi-vehicle interactions on the whole road. By stability analysis, the stability condition of traffic system is obtained. Comparison with stability of full velocity-difference (FVD) model and the completeness of MFVD model are discussed. The mKdV equation is derived from MFVD model through nonlinear analysis to reveal the traffic jams in the form of the kink-antikink density wave. Then the numerical simulation is performed and the results illustrate that the average effect of multi-vehicle interactions plays an important role in effectively suppressing traffic jam. The increase strength of the mean-field velocity difference in MFVD model can rapidly reduce traffic jam and enhance the stability of traffic system.

Research on three-phase traffic flow modeling based on interaction range

NASA Astrophysics Data System (ADS)

Zeng, Jun-Wei; Yang, Xu-Gang; Qian, Yong-Sheng; Wei, Xu-Ting

2017-12-01

On the basis of the multiple velocity difference effect (MVDE) model and under short-range interaction, a new three-phase traffic flow model (S-MVDE) is proposed through careful consideration of the influence of the relationship between the speeds of the two adjacent cars on the running state of the rear car. The random slowing rule in the MVDE model is modified in order to emphasize the influence of vehicle interaction between two vehicles on the probability of vehicles’ deceleration. A single-lane model which without bottleneck structure under periodic boundary conditions is simulated, and it is proved that the traffic flow simulated by S-MVDE model will generate the synchronous flow of three-phase traffic theory. Under the open boundary, the model is expanded by adding an on-ramp, the congestion pattern caused by the bottleneck is simulated at different main road flow rates and on-ramp flow rates, which is compared with the traffic congestion pattern observed by Kerner et al. and it is found that the results are consistent with the congestion characteristics in the three-phase traffic flow theory.

Stochastic Car-Following Model for Explaining Nonlinear Traffic Phenomena

NASA Astrophysics Data System (ADS)

Meng, Jianping; Song, Tao; Dong, Liyun; Dai, Shiqiang

There is a common time parameter for representing the sensitivity or the lag (response) time of drivers in many car-following models. In the viewpoint of traffic psychology, this parameter could be considered as the perception-response time (PRT). Generally, this parameter is set to be a constant in previous models. However, PRT is actually not a constant but a random variable described by the lognormal distribution. Thus the probability can be naturally introduced into car-following models by recovering the probability of PRT. For demonstrating this idea, a specific stochastic model is constructed based on the optimal velocity model. By conducting simulations under periodic boundary conditions, it is found that some important traffic phenomena, such as the hysteresis and phantom traffic jams phenomena, can be reproduced more realistically. Especially, an interesting experimental feature of traffic jams, i.e., two moving jams propagating in parallel with constant speed stably and sustainably, is successfully captured by the present model.

Development and Calibration of Regional Dynamic Traffic Assignment Models for the Estimation of Traffic Performance Measures in Nevada

DOT National Transportation Integrated Search

2017-02-01

This project covered the development and calibration of a Dynamic Traffic Assignment (DTA) model and explained the procedures, constraints, and considerations for usage of this model for the Reno-Sparks area roadway network in Northern Nevada. A lite...

Effect of current vehicle’s interruption on traffic stability in cooperative car-following theory

NASA Astrophysics Data System (ADS)

Zhang, Geng; Liu, Hui

2017-12-01

To reveal the impact of the current vehicle’s interruption information on traffic flow, a new car-following model with consideration of the current vehicle’s interruption is proposed and the influence of the current vehicle’s interruption on traffic stability is investigated through theoretical analysis and numerical simulation. By linear analysis, the linear stability condition of the new model is obtained and the negative influence of the current vehicle’s interruption on traffic stability is shown in the headway-sensitivity space. Through nonlinear analysis, the modified Korteweg-de Vries (mKdV) equation of the new model near the critical point is derived and it can be used to describe the propagating behavior of the traffic density wave. Finally, numerical simulation confirms the analytical results, which shows that the current vehicle’s interruption information can destabilize traffic flow and should be considered in real traffic.

Variable speed limit strategies analysis with mesoscopic traffic flow model based on complex networks

NASA Astrophysics Data System (ADS)

Li, Shu-Bin; Cao, Dan-Ni; Dang, Wen-Xiu; Zhang, Lin

As a new cross-discipline, the complexity science has penetrated into every field of economy and society. With the arrival of big data, the research of the complexity science has reached its summit again. In recent years, it offers a new perspective for traffic control by using complex networks theory. The interaction course of various kinds of information in traffic system forms a huge complex system. A new mesoscopic traffic flow model is improved with variable speed limit (VSL), and the simulation process is designed, which is based on the complex networks theory combined with the proposed model. This paper studies effect of VSL on the dynamic traffic flow, and then analyzes the optimal control strategy of VSL in different network topologies. The conclusion of this research is meaningful to put forward some reasonable transportation plan and develop effective traffic management and control measures to help the department of traffic management.

Framework based on stochastic L-Systems for modeling IP traffic with multifractal behavior

NASA Astrophysics Data System (ADS)

Salvador, Paulo S.; Nogueira, Antonio; Valadas, Rui

2003-08-01

In a previous work we have introduced a multifractal traffic model based on so-called stochastic L-Systems, which were introduced by biologist A. Lindenmayer as a method to model plant growth. L-Systems are string rewriting techniques, characterized by an alphabet, an axiom (initial string) and a set of production rules. In this paper, we propose a novel traffic model, and an associated parameter fitting procedure, which describes jointly the packet arrival and the packet size processes. The packet arrival process is modeled through a L-System, where the alphabet elements are packet arrival rates. The packet size process is modeled through a set of discrete distributions (of packet sizes), one for each arrival rate. In this way the model is able to capture correlations between arrivals and sizes. We applied the model to measured traffic data: the well-known pOct Bellcore, a trace of aggregate WAN traffic and two traces of specific applications (Kazaa and Operation Flashing Point). We assess the multifractality of these traces using Linear Multiscale Diagrams. The suitability of the traffic model is evaluated by comparing the empirical and fitted probability mass and autocovariance functions; we also compare the packet loss ratio and average packet delay obtained with the measured traces and with traces generated from the fitted model. Our results show that our L-System based traffic model can achieve very good fitting performance in terms of first and second order statistics and queuing behavior.

Data Mining for Understanding and Impriving Decision-Making Affecting Ground Delay Programs

NASA Technical Reports Server (NTRS)

Kulkarni, Deepak; Wang, Yao Xun; Sridhar, Banavar

2013-01-01

The continuous growth in the demand for air transportation results in an imbalance between airspace capacity and traffic demand. The airspace capacity of a region depends on the ability of the system to maintain safe separation between aircraft in the region. In addition to growing demand, the airspace capacity is severely limited by convective weather. During such conditions, traffic managers at the FAA's Air Traffic Control System Command Center (ATCSCC) and dispatchers at various Airlines' Operations Center (AOC) collaborate to mitigate the demand-capacity imbalance caused by weather. The end result is the implementation of a set of Traffic Flow Management (TFM) initiatives such as ground delay programs, reroute advisories, flow metering, and ground stops. Data Mining is the automated process of analyzing large sets of data and then extracting patterns in the data. Data mining tools are capable of predicting behaviors and future trends, allowing an organization to benefit from past experience in making knowledge-driven decisions. The work reported in this paper is focused on ground delay programs. Data mining algorithms have the potential to develop associations between weather patterns and the corresponding ground delay program responses. If successful, they can be used to improve and standardize TFM decision resulting in better predictability of traffic flows on days with reliable weather forecasts. The approach here seeks to develop a set of data mining and machine learning models and apply them to historical archives of weather observations and forecasts and TFM initiatives to determine the extent to which the theory can predict and explain the observed traffic flow behaviors.

Impact of real-time traffic characteristics on crash occurrence: Preliminary results of the case of rare events.

PubMed

Theofilatos, Athanasios; Yannis, George; Kopelias, Pantelis; Papadimitriou, Fanis

2018-01-04

Considerable efforts have been made from researchers and policy makers in order to explain road crash occurrence and improve road safety performance of highways. However, there are cases when crashes are so few that they could be considered as rare events. In such cases, the binary dependent variable is characterized by dozens to thousands of times fewer events (crashes) than non-events (non-crashes). This paper attempts to add to the current knowledge by investigating crash likelihood by utilizing real-time traffic data and by proposing a framework driven by appropriate statistical models (Bias Correction and Firth method) in order to overcome the problems that arise when the number of crashes is very low. Under this approach instead of using traditional logistic regression methods, crashes are considered as rare events In order to demonstrate this approach, traffic data were collected from three random loop detectors in the Attica Tollway ("Attiki Odos") located in Greater Athens Area in Greece for the 2008-2011 period. The traffic dataset consists of hourly aggregated traffic data such as flow, occupancy, mean time speed and percentage of trucks in traffic. This study demonstrates the application and findings of our approach and revealed a negative relationship between crash occurrence and speed in crash locations. The method and findings of the study attempt to provide insights on the mechanism of crash occurrence and also to overcome data considerations for the first time in safety evaluation of motorways. Copyright © 2017 Elsevier Ltd. All rights reserved.

Studies of vehicle overtaking dynamics and its influence on traffic flow at a bidirectional road

NASA Astrophysics Data System (ADS)

Echab, H.; Marzoug, R.; Lakouari, N.; Ez-Zahraouy, H.

For the purposes of optimizing traffic flow composed of different types of vehicles, it is important to understand the interactions between them. This paper proposes a cellular automata model to investigate a bidirectional two-lane traffic flow under the periodic boundary condition. The vehicle flux and the phase diagrams of the system in the (ρ1,ρ2) space are constructed by applying two different overtaking models (symmetric, asymmetric). The inter-lane correlation and the overtaking frequency are also studied. The simulation results show that the variation of the density of one lane has an apparent influence on the traffic of the adjacent lane. Furthermore, it is found that the phase diagram on both models is classified into several regions. Thus, for the symmetric model, as the overtaking probability increases, the traffic on the system becomes better. Likewise, the results also indicate that the asymmetric model can effectively enhance the traffic capacity and alleviate the congested state.

Life Times of Simulated Traffic Jams

NASA Astrophysics Data System (ADS)

Nagel, Kai

We study a model for freeway traffic which includes strong noise taking into account the fluctuations of individual driving behavior. The model shows emergent traffic jams with a self-similar appearance near the throughput maximum of the traffic. The lifetime distribution of these jams shows a short scaling regime, which gets considerably longer if one reduces the fluctuations when driving at maximum speed but leaves the fluctuations for slowing down or accelerating unchanged. The outflow from a traffic jam self-organizes into this state of maximum throughput.

Aircraft/Air Traffic Management Functional Analysis Model: Technical Description. 2.0

NASA Technical Reports Server (NTRS)

Etheridge, Melvin; Plugge, Joana; Retina, Nusrat

1998-01-01

The Aircraft/Air Traffic Management Functional Analysis Model, Version 2.0 (FAM 2.0), is a discrete event simulation model designed to support analysis of alternative concepts in air traffic management and control. FAM 2.0 was developed by the Logistics Management Institute (LMI) under a National Aeronautics and Space Administration (NASA) contract. This document provides a technical description of FAM 2.0 and its computer files to enable the modeler and programmer to make enhancements or modifications to the model. Those interested in a guide for using the model in analysis should consult the companion document, Aircraft/Air Traffic Management Functional Analysis Model, Version 2.0 Users Manual.

Vehicle-to-vehicle communications in mixed passenger-freight convoys : [research brief].

DOT National Transportation Integrated Search

2016-09-01

Freight traffic in California depends, to a large degree, on trucks, due to their flexibility in : visiting warehouses, distribution centers, and terminals. However, trucks pose major challenges : to traffic and road safety. Accidents involving truck...

Seismic vulnerability analysis of bridges in mountainous states.

DOT National Transportation Integrated Search

2013-09-01

Depending on the location, highway bridges can often support considerable amounts of traffic. Due to the limitations on current earthquake forecasting techniques, a normal amount of traffic will also typically remain on a bridge when an earthquake oc...

An agent-based model for queue formation of powered two-wheelers in heterogeneous traffic

NASA Astrophysics Data System (ADS)

Lee, Tzu-Chang; Wong, K. I.

2016-11-01

This paper presents an agent-based model (ABM) for simulating the queue formation of powered two-wheelers (PTWs) in heterogeneous traffic at a signalized intersection. The main novelty is that the proposed interaction rule describing the position choice behavior of PTWs when queuing in heterogeneous traffic can capture the stochastic nature of the decision making process. The interaction rule is formulated as a multinomial logit model, which is calibrated by using a microscopic traffic trajectory dataset obtained from video footage. The ABM is validated against the survey data for the vehicular trajectory patterns, queuing patterns, queue lengths, and discharge rates. The results demonstrate that the proposed model is capable of replicating the observed queue formation process for heterogeneous traffic.

Variable cycle control model for intersection based on multi-source information

NASA Astrophysics Data System (ADS)

Sun, Zhi-Yuan; Li, Yue; Qu, Wen-Cong; Chen, Yan-Yan

2018-05-01

In order to improve the efficiency of traffic control system in the era of big data, a new variable cycle control model based on multi-source information is presented for intersection in this paper. Firstly, with consideration of multi-source information, a unified framework based on cyber-physical system is proposed. Secondly, taking into account the variable length of cell, hysteresis phenomenon of traffic flow and the characteristics of lane group, a Lane group-based Cell Transmission Model is established to describe the physical properties of traffic flow under different traffic signal control schemes. Thirdly, the variable cycle control problem is abstracted into a bi-level programming model. The upper level model is put forward for cycle length optimization considering traffic capacity and delay. The lower level model is a dynamic signal control decision model based on fairness analysis. Then, a Hybrid Intelligent Optimization Algorithm is raised to solve the proposed model. Finally, a case study shows the efficiency and applicability of the proposed model and algorithm.

Modeling hurricane evacuation traffic : a mobile real-time traffic counter for monitoring hurricane evacuation traffic conditions.

DOT National Transportation Integrated Search

2006-04-01

In this research report, an investigation was conducted to identify a suitable traffic monitoring device for collecting traffic data during actual emergency evacuation conditions that may result from hurricanes in Louisiana. The study reviewed thorou...

Joint parameter and state estimation algorithms for real-time traffic monitoring.

DOT National Transportation Integrated Search

2013-12-01

A common approach to traffic monitoring is to combine a macroscopic traffic flow model with traffic sensor data in a process called state estimation, data fusion, or data assimilation. The main challenge of traffic state estimation is the integration...

Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information.

PubMed

Pan, Long; Yao, Enjian; Yang, Yang

2016-12-01

With the rapid development of urbanization and motorization in China, traffic-related air pollution has become a major component of air pollution which constantly jeopardizes public health. This study proposes an integrated framework for estimating the concentration of traffic-related air pollution with real-time traffic and basic meteorological information and also for further evaluating the impact of traffic-related air pollution. First, based on the vehicle emission factor models sensitive to traffic status, traffic emissions are calculated according to the real-time link-based average traffic speed, traffic volume, and vehicular fleet composition. Then, based on differences in meteorological conditions, traffic pollution sources are divided into line sources and point sources, and the corresponding methods to determine the dynamic affecting areas are also proposed. Subsequently, with basic meteorological data, Gaussian dispersion model and puff integration model are applied respectively to estimate the concentration of traffic-related air pollution. Finally, the proposed estimating framework is applied to calculate the distribution of CO concentration in the main area of Beijing, and the population exposure is also calculated to evaluate the impact of traffic-related air pollution on public health. Results show that there is a certain correlation between traffic indicators (i.e., traffic speed and traffic intensity) of the affecting area and traffic-related CO concentration of the target grid, which indicates the methods to determine the affecting areas are reliable. Furthermore, the reliability of the proposed estimating framework is verified by comparing the predicted and the observed ambient CO concentration. In addition, results also show that the traffic-related CO concentration is higher in morning and evening peak hours, and has a heavier impact on public health within the Fourth Ring Road of Beijing due to higher population density and higher CO concentration under calm wind condition in this area. Copyright © 2016 Elsevier Ltd. All rights reserved.

A model of traffic signs recognition with convolutional neural network

NASA Astrophysics Data System (ADS)

Hu, Haihe; Li, Yujian; Zhang, Ting; Huo, Yi; Kuang, Wenqing

2016-10-01

In real traffic scenes, the quality of captured images are generally low due to some factors such as lighting conditions, and occlusion on. All of these factors are challengeable for automated recognition algorithms of traffic signs. Deep learning has provided a new way to solve this kind of problems recently. The deep network can automatically learn features from a large number of data samples and obtain an excellent recognition performance. We therefore approach this task of recognition of traffic signs as a general vision problem, with few assumptions related to road signs. We propose a model of Convolutional Neural Network (CNN) and apply the model to the task of traffic signs recognition. The proposed model adopts deep CNN as the supervised learning model, directly takes the collected traffic signs image as the input, alternates the convolutional layer and subsampling layer, and automatically extracts the features for the recognition of the traffic signs images. The proposed model includes an input layer, three convolutional layers, three subsampling layers, a fully-connected layer, and an output layer. To validate the proposed model, the experiments are implemented using the public dataset of China competition of fuzzy image processing. Experimental results show that the proposed model produces a recognition accuracy of 99.01 % on the training dataset, and yield a record of 92% on the preliminary contest within the fourth best.

Evaluation of the impacts of traffic states on crash risks on freeways.

PubMed

Xu, Chengcheng; Liu, Pan; Wang, Wei; Li, Zhibin

2012-07-01

The primary objective of this study is to divide freeway traffic flow into different states, and to evaluate the safety performance associated with each state. Using traffic flow data and crash data collected from a northbound segment of the I-880 freeway in the state of California, United States, K-means clustering analysis was conducted to classify traffic flow into five different states. Conditional logistic regression models using case-controlled data were then developed to study the relationship between crash risks and traffic states. Traffic flow characteristics in each traffic state were compared to identify the underlying phenomena that made certain traffic states more hazardous than others. Crash risk models were also developed for different traffic states to identify how traffic flow characteristics such as speed and speed variance affected crash risks in different traffic states. The findings of this study demonstrate that the operations of freeway traffic can be divided into different states using traffic occupancy measured from nearby loop detector stations, and each traffic state can be assigned with a certain safety level. The impacts of traffic flow parameters on crash risks are different across different traffic flow states. A method based on discriminant analysis was further developed to identify traffic states given real-time freeway traffic flow data. Validation results showed that the method was of reasonably high accuracy for identifying freeway traffic states. Copyright © 2012 Elsevier Ltd. All rights reserved.

FHWA traffic noise model, version 1.0 : user's guide

DOT National Transportation Integrated Search

1998-01-01

This User's Guide is for the Federal Highway Administration's Traffic Noise Model (FHWA TNM), Version 1.0 -- the FHWAs computer program for highway traffic noise prediction and analysis. Two companion reports, a Technical Manual and a data repor...

Value of Information and Information Services

DOT National Transportation Integrated Search

1975-10-01

The report describes the salient features of the SCOT (Simulation of Corridor Traffic) model and a successful calibration and validation. SCOT is a computer model that may be applied to an urban traffic corridor and will simulate vehicular traffic on...

Application of dynamic traffic assignment to advanced managed lane modeling.

DOT National Transportation Integrated Search

2013-11-01

In this study, a demand estimation framework is developed for assessing the managed lane (ML) : strategies by utilizing dynamic traffic assignment (DTA) modeling, instead of the traditional : approaches that are based on the static traffic assignment...

FHWA Traffic Noise Model, version 1.0 technical manual

DOT National Transportation Integrated Search

1998-02-01

This Technical Manual is for the Federal Highway Administrations Traffic Noise Model (FHWA TNM), Version 1.0 -- the FHWAs computer program for highway traffic noise prediction and analysis. Two companion reports, a Users Guide and a data r...

Traffic flow simulation for an urban freeway corridor

DOT National Transportation Integrated Search

1998-01-01

The objective of this paper is to develop a realistic and operational macroscopic traffic flow simulation model which requires relatively less data collection efforts. Such a model should be capable of delineating the dynamics of traffic flow created...

Exact results of 1D traffic cellular automata: The low-density behavior of the Fukui-Ishibashi model

NASA Astrophysics Data System (ADS)

Salcido, Alejandro; Hernández-Zapata, Ernesto; Carreón-Sierra, Susana

2018-03-01

The maximum entropy states of the cellular automata models for traffic flow in a single-lane with no anticipation are presented and discussed. The exact analytical solutions for the low-density behavior of the stochastic Fukui-Ishibashi traffic model were obtained and compared with computer simulations of the model. An excellent agreement was found.

Application of a Three-Layer Photochemical Box Model in an Athens Street Canyon.

PubMed

Proyou, Athena G; Ziomas, Loannis C; Stathopoulos, Antony

1998-05-01

The aim of this paper is to show that a photochemical box model could describe the air pollution diurnal profiles within a typical street canyon in the city of Athens. As sophisticated three-dimensional dispersion models are computationally expensive and they cannot serve to simulate pollution levels in the scale of an urban street canyon, a suitably modified three-layer photochemical box model was applied. A street canyon of Athens with heavy traffic was chosen to apply the aforementioned model. The model was used to calculate pollutant concentrations during two days with meteorological conditions favoring pollutant accumulation. Road traffic emissions were calculated based on existing traffic load measurements. Meteorological data, as well as various pollutant concentrations, in order to compare with the model results, were provided by available measurements. The calculated concentrations were found to be in good agreement with measured concentration levels and show that, when traffic load and traffic composition data are available, this model can be used to predict pollution episodes. It is noteworthy that high concentrations persisted, even after additional traffic restriction measures were taken on the second day because of the high pollution levels.

Effects of canyon geometry on the distribution of traffic-related air pollution in a large urban area: Implications of a multi-canyon air pollution dispersion model

NASA Astrophysics Data System (ADS)

Fu, Xiangwen; Liu, Junfeng; Ban-Weiss, George A.; Zhang, Jiachen; Huang, Xin; Ouyang, Bin; Popoola, Olalekan; Tao, Shu

2017-09-01

Street canyons are ubiquitous in urban areas. Traffic-related air pollutants in street canyons can adversely affect human health. In this study, an urban-scale traffic pollution dispersion model is developed considering street distribution, canyon geometry, background meteorology, traffic assignment, traffic emissions and air pollutant dispersion. In the model, vehicle exhausts generated from traffic flows first disperse inside street canyons along the micro-scale wind field generated by computational fluid dynamics (CFD) model. Then, pollutants leave the street canyon and further disperse over the urban area. On the basis of this model, the effects of canyon geometry on the distribution of NOx and CO from traffic emissions were studied over the center of Beijing. We found that an increase in building height leads to heavier pollution inside canyons and lower pollution outside canyons at pedestrian level, resulting in higher domain-averaged concentrations over the area. In addition, canyons with highly even or highly uneven building heights on each side of the street tend to lower the urban-scale air pollution concentrations at pedestrian level. Further, increasing street widths tends to lead to lower pollutant concentrations by reducing emissions and enhancing ventilation simultaneously. Our results indicate that canyon geometry strongly influences human exposure to traffic pollutants in the populated urban area. Carefully planning street layout and canyon geometry while considering traffic demand as well as local weather patterns may significantly reduce inhalation of unhealthy air by urban residents.

Road traffic noise prediction model for heterogeneous traffic based on ASJ-RTN Model 2008 with consideration of horn

NASA Astrophysics Data System (ADS)

Hustim, M.; Arifin, Z.; Aly, S. H.; Ramli, M. I.; Zakaria, R.; Liputo, A.

2018-04-01

This research aimed to predict the noise produced by the traffic in the road network in Makassar City using ASJ-RTN Model 2008 by calculating the horn sound. Observations were taken at 37 survey points on road side. The observations were conducted at 06.00 - 18.00 and 06.00 - 21.00 which research objects were motorcycle (MC), light vehicle (LV) and heavy vehicle (HV). The observed data were traffic volume, vehicle speed, number of horn and traffic noise using Sound Level Meter Tenmars TM-103. The research result indicates that prediction noise model by calculating the horn sound produces the average noise level value of 78.5 dB having the Pearson’s correlation and RMSE of 0.95 and 0.87. Therefore, ASJ-RTN Model 2008 prediction model by calculating the horn sound is said to be sufficiently good for predicting noise level.

Preliminary Benefits Assessment of Traffic Aware Strategic Aircrew Requests (TASAR)

NASA Technical Reports Server (NTRS)

Henderson, Jeff; Idris, Husni; Wing, David J.

2012-01-01

While en route, aircrews submit trajectory change requests to air traffic control (ATC) to better meet their objectives including reduced delays, reduced fuel burn, and passenger comfort. Aircrew requests are currently made with limited to no information on surrounding traffic. Consequently, these requests are uninformed about a key ATC objective, ensuring traffic separation, and therefore less likely to be accepted than requests informed by surrounding traffic and that avoids creating conflicts. This paper studies the benefits of providing aircrews with on-board decision support to generate optimized trajectory requests that are probed and cleared of known separation violations prior to issuing the request to ATC. These informed requests are referred to as traffic aware strategic aircrew requests (TASAR) and leverage traffic surveillance information available through Automatic Dependent Surveillance Broadcast (ADS-B) In capability. Preliminary fast-time simulation results show increased benefits with longer stage lengths since beneficial trajectory changes can be applied over a longer distance. Also, larger benefits were experienced between large hub airports as compared to other airport sizes. On average, an aircraft equipped with TASAR reduced its travel time by about one to four minutes per operation and fuel burn by about 50 to 550 lbs per operation depending on the objective of the aircrew (time, fuel, or weighted combination of time and fuel), class of airspace user, and aircraft type. These preliminary results are based on analysis of approximately one week of traffic in July 2012 and additional analysis is planned on a larger data set to confirm these initial findings.

Instability of cooperative adaptive cruise control traffic flow: A macroscopic approach

NASA Astrophysics Data System (ADS)

Ngoduy, D.

2013-10-01

This paper proposes a macroscopic model to describe the operations of cooperative adaptive cruise control (CACC) traffic flow, which is an extension of adaptive cruise control (ACC) traffic flow. In CACC traffic flow a vehicle can exchange information with many preceding vehicles through wireless communication. Due to such communication the CACC vehicle can follow its leader at a closer distance than the ACC vehicle. The stability diagrams are constructed from the developed model based on the linear and nonlinear stability method for a certain model parameter set. It is found analytically that CACC vehicles enhance the stabilization of traffic flow with respect to both small and large perturbations compared to ACC vehicles. Numerical simulation is carried out to support our analytical findings. Based on the nonlinear stability analysis, we will show analytically and numerically that the CACC system better improves the dynamic equilibrium capacity over the ACC system. We have argued that in parallel to microscopic models for CACC traffic flow, the newly developed macroscopic will provide a complete insight into the dynamics of intelligent traffic flow.

A critical review of principal traffic noise models: Strategies and implications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garg, Naveen, E-mail: ngarg@mail.nplindia.ernet.in; Department of Mechanical, Production and Industrial Engineering, Delhi Technological University, Delhi 110042; Maji, Sagar

2014-04-01

The paper presents an exhaustive comparison of principal traffic noise models adopted in recent years in developed nations. The comparison is drawn on the basis of technical attributes including source modelling and sound propagation algorithms. Although the characterization of source in terms of rolling and propulsion noise in conjunction with advanced numerical methods for sound propagation has significantly reduced the uncertainty in traffic noise predictions, the approach followed is quite complex and requires specialized mathematical skills for predictions which is sometimes quite cumbersome for town planners. Also, it is sometimes difficult to follow the best approach when a variety ofmore » solutions have been proposed. This paper critically reviews all these aspects pertaining to the recent models developed and adapted in some countries and also discusses the strategies followed and implications of these models. - Highlights: • Principal traffic noise models developed are reviewed. • Sound propagation algorithms used in traffic noise models are compared. • Implications of models are discussed.« less

Characterize older driver behavior for traffic simulation and vehicle emission model.

DOT National Transportation Integrated Search

2012-05-01

The use of traffic simulation models is becoming more widespread as a means of : assessing traffic, safety and environmental impacts as a result of infrastructure, control and : operational changes at disaggregate levels. It is imperative that these ...

Gulf Coast megaregion evacuation traffic simulation modeling and analysis.

DOT National Transportation Integrated Search

2015-12-01

This paper describes a project to develop a micro-level traffic simulation for a megaregion. To : accomplish this, a mass evacuation event was modeled using a traffic demand generation process that : created a spatial and temporal distribution of dep...

FHWA Traffic Noise Model user's guide (version 2.0 addendum).

DOT National Transportation Integrated Search

2002-03-01

In March 1998, the Federal Highway Administration (FHWA) Office of Natural : Environment, released the FHWA Traffic Noise Model (FHWA TNM) Version 1.0, a : state-of-the-art computer program for highway traffic noise prediction and : analysis. Since t...

First Coast Guard district traffic model report

DOT National Transportation Integrated Search

1997-11-01

The purpose of this report was to describe the methodology used in developing the First Coast Guard District (CGD1) Traffic Model and to document the potential National Distress System (NDS) voice and data traffic forecasted for the year 2001. The ND...

A Framework for Validating Traffic Simulation Models at the Vehicle Trajectory Level

DOT National Transportation Integrated Search

2017-03-01

Based on current practices, traffic simulation models are calibrated and validated using macroscopic measures such as 15-minute averages of traffic counts or average point-to-point travel times. For an emerging number of applications, including conne...

Analysis on the Correlation of Traffic Flow in Hainan Province Based on Baidu Search

NASA Astrophysics Data System (ADS)

Chen, Caixia; Shi, Chun

2018-03-01

Internet search data records user’s search attention and consumer demand, providing necessary database for the Hainan traffic flow model. Based on Baidu Index, with Hainan traffic flow as example, this paper conduct both qualitative and quantitative analysis on the relationship between search keyword from Baidu Index and actual Hainan tourist traffic flow, and build multiple regression model by SPSS.

Air pollution and survival within the Washington University-EPRI veterans cohort: risks based on modeled estimates of ambient levels of hazardous and criteria air pollutants.

PubMed

Lipfert, Frederick W; Wyzga, Ronald E; Baty, Jack D; Miller, J Philip

2009-04-01

For this paper, we considered relationships between mortality, vehicular traffic density, and ambient levels of 12 hazardous air pollutants, elemental carbon (EC), oxides of nitrogen (NOx), sulfur dioxide (SO2), and sulfate (SO4(2-)). These pollutant species were selected as markers for specific types of emission sources, including vehicular traffic, coal combustion, smelters, and metal-working industries. Pollutant exposures were estimated using emissions inventories and atmospheric dispersion models. We analyzed associations between county ambient levels of these pollutants and survival patterns among approximately 70,000 U.S. male veterans by mortality period (1976-2001 and subsets), type of exposure model, and traffic density level. We found significant associations between all-cause mortality and traffic-related air quality indicators and with traffic density per se, with stronger associations for benzene, formaldehyde, diesel particulate, NOx, and EC. The maximum effect on mortality for all cohort subjects during the 26-yr follow-up period is approximately 10%, but most of the pollution-related deaths in this cohort occurred in the higher-traffic counties, where excess risks approach 20%. However, mortality associations with diesel particulates are similar in high- and low-traffic counties. Sensitivity analyses show risks decreasing slightly over time and minor differences between linear and logarithmic exposure models. Two-pollutant models show stronger risks associated with specific traffic-related pollutants than with traffic density per se, although traffic density retains statistical significance in most cases. We conclude that tailpipe emissions of both gases and particles are among the most significant and robust predictors of mortality in this cohort and that most of those associations have weakened over time. However, we have not evaluated possible contributions from road dust or traffic noise. Stratification by traffic density level suggests the presence of response thresholds, especially for gaseous pollutants. Because of their wider distributions of estimated exposures, risk estimates based on emissions and atmospheric dispersion models tend to be more precise than those based on local ambient measurements.

Validity of clinical color vision tests for air traffic control specialists.

DOT National Transportation Integrated Search

1992-10-01

An experiment on the relationship between aeromedical color vision screening test performance and performance on color-dependent tasks of Air Traffic Control Specialists was replicated to expand the data base supporting the job-related validity of th...

A two-lane cellular automaton traffic flow model with the influence of driver, vehicle and road

NASA Astrophysics Data System (ADS)

Zhao, Han-Tao; Nie, Cen; Li, Jing-Ru; Wei, Yu-Ao

2016-07-01

On the basis of one-lane comfortable driving model, this paper established a two-lane traffic cellular automata model, which improves the slow randomization effected by brake light. Considering the driver psychological characteristics and mixed traffic, we studied the lateral influence between vehicles on adjacent lanes. Through computer simulation, the space-time diagram and the fundamental figure under different conditions are obtained. The study found that aggressive driver makes a slight congestion in low-density traffic and improves the capacity of high-density traffic, when the density exceeds 20pcu/km the more aggressive drivers the greater the flow, when the density below 40pcu/km driver character makes an effect, the more cautious driver, the lower the flow. The ratio of big cars has the same effect as the ratio of aggressive drivers. Brake lights have the greatest impact on traffic flow and when the density exceeds 10pcu/km the traffic flow fluctuates. Under periodic boundary conditions, the disturbance of road length on traffic is minimal. The lateral influence only play a limited role in the medium-density conditions, and only affect the average speed of traffic at low density.

Satellite switched FDMA advanced communication technology satellite program

NASA Technical Reports Server (NTRS)

Atwood, S.; Higton, G. H.; Wood, K.; Kline, A.; Furiga, A.; Rausch, M.; Jan, Y.

1982-01-01

The satellite switched frequency division multiple access system provided a detailed system architecture that supports a point to point communication system for long haul voice, video and data traffic between small Earth terminals at Ka band frequencies at 30/20 GHz. A detailed system design is presented for the space segment, small terminal/trunking segment at network control segment for domestic traffic model A or B, each totaling 3.8 Gb/s of small terminal traffic and 6.2 Gb/s trunk traffic. The small terminal traffic (3.8 Gb/s) is emphasized, for the satellite router portion of the system design, which is a composite of thousands of Earth stations with digital traffic ranging from a single 32 Kb/s CVSD voice channel to thousands of channels containing voice, video and data with a data rate as high as 33 Mb/s. The system design concept presented, effectively optimizes a unique frequency and channelization plan for both traffic models A and B with minimum reorganization of the satellite payload transponder subsystem hardware design. The unique zoning concept allows multiple beam antennas while maximizing multiple carrier frequency reuse. Detailed hardware design estimates for an FDMA router (part of the satellite transponder subsystem) indicate a weight and dc power budget of 353 lbs, 195 watts for traffic model A and 498 lbs, 244 watts for traffic model B.

Safety performance of traffic phases and phase transitions in three phase traffic theory.

PubMed

Xu, Chengcheng; Liu, Pan; Wang, Wei; Li, Zhibin

2015-12-01

Crash risk prediction models were developed to link safety to various phases and phase transitions defined by the three phase traffic theory. Results of the Bayesian conditional logit analysis showed that different traffic states differed distinctly with respect to safety performance. The random-parameter logit approach was utilized to account for the heterogeneity caused by unobserved factors. The Bayesian inference approach based on the Markov Chain Monte Carlo (MCMC) method was used for the estimation of the random-parameter logit model. The proposed approach increased the prediction performance of the crash risk models as compared with the conventional logit model. The three phase traffic theory can help us better understand the mechanism of crash occurrences in various traffic states. The contributing factors to crash likelihood can be well explained by the mechanism of phase transitions. We further discovered that the free flow state can be divided into two sub-phases on the basis of safety performance, including a true free flow state in which the interactions between vehicles are minor, and a platooned traffic state in which bunched vehicles travel in successions. The results of this study suggest that a safety perspective can be added to the three phase traffic theory. The results also suggest that the heterogeneity between different traffic states should be considered when estimating the risks of crash occurrences on freeways. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modeling temporal and spatial variability of traffic-related air pollution: Hourly land use regression models for black carbon

NASA Astrophysics Data System (ADS)

Dons, Evi; Van Poppel, Martine; Kochan, Bruno; Wets, Geert; Int Panis, Luc

2013-08-01

Land use regression (LUR) modeling is a statistical technique used to determine exposure to air pollutants in epidemiological studies. Time-activity diaries can be combined with LUR models, enabling detailed exposure estimation and limiting exposure misclassification, both in shorter and longer time lags. In this study, the traffic related air pollutant black carbon was measured with μ-aethalometers on a 5-min time base at 63 locations in Flanders, Belgium. The measurements show that hourly concentrations vary between different locations, but also over the day. Furthermore the diurnal pattern is different for street and background locations. This suggests that annual LUR models are not sufficient to capture all the variation. Hourly LUR models for black carbon are developed using different strategies: by means of dummy variables, with dynamic dependent variables and/or with dynamic and static independent variables. The LUR model with 48 dummies (weekday hours and weekend hours) performs not as good as the annual model (explained variance of 0.44 compared to 0.77 in the annual model). The dataset with hourly concentrations of black carbon can be used to recalibrate the annual model, resulting in many of the original explaining variables losing their statistical significance, and certain variables having the wrong direction of effect. Building new independent hourly models, with static or dynamic covariates, is proposed as the best solution to solve these issues. R2 values for hourly LUR models are mostly smaller than the R2 of the annual model, ranging from 0.07 to 0.8. Between 6 a.m. and 10 p.m. on weekdays the R2 approximates the annual model R2. Even though models of consecutive hours are developed independently, similar variables turn out to be significant. Using dynamic covariates instead of static covariates, i.e. hourly traffic intensities and hourly population densities, did not significantly improve the models' performance.

Identifying crash-prone traffic conditions under different weather on freeways.

PubMed

Xu, Chengcheng; Wang, Wei; Liu, Pan

2013-09-01

Understanding the relationships between traffic flow characteristics and crash risk under adverse weather conditions will help highway agencies develop proactive safety management strategies to improve traffic safety in adverse weather conditions. The primary objective is to develop separate crash risk prediction models for different weather conditions. The crash data, weather data, and traffic data used in this study were collected on the I-880N freeway in California in 2008 and 2010. This study considered three different weather conditions: clear weather, rainy weather, and reduced visibility weather. The preliminary analysis showed that there was some heterogeneity in the risk estimates for traffic flow characteristics by weather conditions, and that the crash risk prediction model for all weather conditions cannot capture the impacts of the traffic flow variables on crash risk under adverse weather conditions. The Bayesian random intercept logistic regression models were applied to link the likelihood of crash occurrence with various traffic flow characteristics under different weather conditions. The crash risk prediction models were compared to their corresponding logistic regression model. It was found that the random intercept model improved the goodness-of-fit of the crash risk prediction models. The model estimation results showed that the traffic flow characteristics contributing to crash risk were different across different weather conditions. The speed difference between upstream and downstream stations was found to be significant in each crash risk prediction model. Speed difference between upstream and downstream stations had the largest impact on crash risk in reduced visibility weather, followed by that in rainy weather. The ROC curves were further developed to evaluate the predictive performance of the crash risk prediction models under different weather conditions. The predictive performance of the crash risk model for clear weather was better than those of the crash risk models for adverse weather conditions. The research results could promote a better understanding of the impacts of traffic flow characteristics on crash risk under adverse weather conditions, which will help transportation professionals to develop better crash prevention strategies in adverse weather. Copyright © 2013 National Safety Council and Elsevier Ltd. All rights reserved.

Impact of traffic composition on accessibility as indicator of transport sustainability

NASA Astrophysics Data System (ADS)

Nahdalina; Hadiwardoyo, S. P.; Nahry

2017-05-01

Sustainable transport is closely related to quality of life in the community at present and in the future. Some indicators of transport sustainability are accessibility measurement of origin/destination, the operating costs of transport (vehicle operating cost or VOC) and external transportation costs (emission cost). The indicators could be combined into accessibility measurement model. In other case, almost traffic congestion occurred on the condition of mixed traffic. This paper aimed to analyse the indicator of transport sustainability through simulation under condition of various traffic composition. Various composition of truck to total traffic flow are 0%, 10% and 20%. Speed and V/C are calculated from traffic flow to estimate the VOC and emission cost. 5 VOC components and 3 types of emission cost (CO2, CH4 and N2O) are counted to be a travel cost. Accessibility measurement was calculated using travel cost and gravity model approaches. Result of the research shows that the total traffic flow has indirect impact on accessibility measurement if using travel cost approach. Meanwhile, the composition of traffic flow has an affect on accessibility measurement if using gravity model approach.

Near real-time traffic routing

NASA Technical Reports Server (NTRS)

Yang, Chaowei (Inventor); Xie, Jibo (Inventor); Zhou, Bin (Inventor); Cao, Ying (Inventor)

2012-01-01

A near real-time physical transportation network routing system comprising: a traffic simulation computing grid and a dynamic traffic routing service computing grid. The traffic simulator produces traffic network travel time predictions for a physical transportation network using a traffic simulation model and common input data. The physical transportation network is divided into a multiple sections. Each section has a primary zone and a buffer zone. The traffic simulation computing grid includes multiple of traffic simulation computing nodes. The common input data includes static network characteristics, an origin-destination data table, dynamic traffic information data and historical traffic data. The dynamic traffic routing service computing grid includes multiple dynamic traffic routing computing nodes and generates traffic route(s) using the traffic network travel time predictions.

Lower limb and associated injuries in frontal-impact road traffic collisions.

PubMed

Ammori, Mohannad B; Eid, Hani O; Abu-Zidan, Fikri M

2016-03-01

To study the relationship between severity of injury of the lower limb and severity of injury of the head, thoracic, and abdominal regions in frontal-impact road traffic collisions. Consecutive hospitalised trauma patients who were involved in a frontal road traffic collision were prospectively studied over 18 months. Patients with at least one Abbreviated Injury Scale (AIS) ≥3 or AIS 2 injuries within two AIS body regions were included. Patients were divided into two groups depending on the severity of injury to the head, chest or abdomen. Low severity group had an AIS < 2 and high severity group had an AIS ≥ 2. Backward likelihood logistic regression models were used to define significant factors affecting the severity of head, chest or abdominal injuries. Eighty-five patients were studied. The backward likelihood logistic regression model defining independent factors affecting severity of head injuries was highly significant (p =0.01, nagelkerke r square = 0.1) severity of lower limb injuries was the only significant factor (p=0.013) having a negative correlation with head injury (Odds ratio of 0.64 (95% CI: 0.45-0.91). Occupants who sustain a greater severity of injury to the lower limb in a frontal-impact collision are likely to be spared from a greater severity of head injury.

Crash Frequency Modeling Using Real-Time Environmental and Traffic Data and Unbalanced Panel Data Models

PubMed Central

Chen, Feng; Chen, Suren; Ma, Xiaoxiang

2016-01-01

Traffic and environmental conditions (e.g., weather conditions), which frequently change with time, have a significant impact on crash occurrence. Traditional crash frequency models with large temporal scales and aggregated variables are not sufficient to capture the time-varying nature of driving environmental factors, causing significant loss of critical information on crash frequency modeling. This paper aims at developing crash frequency models with refined temporal scales for complex driving environments, with such an effort providing more detailed and accurate crash risk information which can allow for more effective and proactive traffic management and law enforcement intervention. Zero-inflated, negative binomial (ZINB) models with site-specific random effects are developed with unbalanced panel data to analyze hourly crash frequency on highway segments. The real-time driving environment information, including traffic, weather and road surface condition data, sourced primarily from the Road Weather Information System, is incorporated into the models along with site-specific road characteristics. The estimation results of unbalanced panel data ZINB models suggest there are a number of factors influencing crash frequency, including time-varying factors (e.g., visibility and hourly traffic volume) and site-varying factors (e.g., speed limit). The study confirms the unique significance of the real-time weather, road surface condition and traffic data to crash frequency modeling. PMID:27322306

FHWA Traffic Noise Model version 1.1 user's guide (Addendum)

DOT National Transportation Integrated Search

2000-09-30

In March 1998, the Federal Highway Administration (FHWA) Office of Natural Environment, released the FHWA Traffic Noise Model (FHWA TNM) Version 1.0, a state-of-the-art computer program for highway traffic noise prediction and analysis. Since then, t...

Optimal loop placement and models for length - based vehicle classification and stop - and - go traffic.

DOT National Transportation Integrated Search

2011-01-01

Inductive loops are widely used nationwide for traffic monitoring as a data source for a variety of : needs in generating traffic information for operation and planning analysis, validations of travel : demand models, freight studies, pavement design...

Proof of Concept for the Trajectory-Level Validation Framework for Traffic Simulation Models

DOT National Transportation Integrated Search

2017-10-30

Based on current practices, traffic simulation models are calibrated and validated using macroscopic measures such as 15-minute averages of traffic counts or average point-to-point travel times. For an emerging number of applications, including conne...

Switching performance of OBS network model under prefetched real traffic

NASA Astrophysics Data System (ADS)

Huang, Zhenhua; Xu, Du; Lei, Wen

2005-11-01

Optical Burst Switching (OBS) [1] is now widely considered as an efficient switching technique in building the next generation optical Internet .So it's very important to precisely evaluate the performance of the OBS network model. The performance of the OBS network model is variable in different condition, but the most important thing is that how it works under real traffic load. In the traditional simulation models, uniform traffics are usually generated by simulation software to imitate the data source of the edge node in the OBS network model, and through which the performance of the OBS network is evaluated. Unfortunately, without being simulated by real traffic, the traditional simulation models have several problems and their results are doubtable. To deal with this problem, we present a new simulation model for analysis and performance evaluation of the OBS network, which uses prefetched IP traffic to be data source of the OBS network model. The prefetched IP traffic can be considered as real IP source of the OBS edge node and the OBS network model has the same clock rate with a real OBS system. So it's easy to conclude that this model is closer to the real OBS system than the traditional ones. The simulation results also indicate that this model is more accurate to evaluate the performance of the OBS network system and the results of this model are closer to the actual situation.

Modeling effects of traffic and landscape characteristics on ambient nitrogen dioxide levels in Connecticut

NASA Astrophysics Data System (ADS)

Skene, Katherine J.; Gent, Janneane F.; McKay, Lisa A.; Belanger, Kathleen; Leaderer, Brian P.; Holford, Theodore R.

2010-12-01

An integrated exposure model was developed that estimates nitrogen dioxide (NO 2) concentration at residences using geographic information systems (GIS) and variables derived within residential buffers representing traffic volume and landscape characteristics including land use, population density and elevation. Multiple measurements of NO 2 taken outside of 985 residences in Connecticut were used to develop the model. A second set of 120 outdoor NO 2 measurements as well as cross-validation were used to validate the model. The model suggests that approximately 67% of the variation in NO 2 levels can be explained by: traffic and land use primarily within 2 km of a residence; population density; elevation; and time of year. Potential benefits of this model for health effects research include improved spatial estimations of traffic-related pollutant exposure and reduced need for extensive pollutant measurements. The model, which could be calibrated and applied in areas other than Connecticut, has importance as a tool for exposure estimation in epidemiological studies of traffic-related air pollution.

The impact of road traffic noise on cognitive performance in attention-based tasks depends on noise level even within moderate-level ranges

PubMed Central

Schlittmeier, Sabine J.; Feil, Alexandra; Liebl, Andreas; Hellbrück, Jürgen

2015-01-01

Little empirical evidence is available regarding the effects of road traffic noise on cognitive performance in adults, although traffic noise can be heard at many offices and home office workplaces. Our study tested the impact of road traffic noise at different levels (50 dB(A), 60 dB(A), 70 dB(A)) on performance in three tasks that differed with respect to their dependency on attentional and storage functions, as follows: The Stroop task, in which performance relied predominantly on attentional functions (e.g., inhibition of automated responses; Experiment 1: n = 24); a non-automated multistage mental arithmetic task calling for both attentional and storage functions (Exp. 2: n = 18); and verbal serial recall, which placed a burden predominantly on storage functions (Experiment 3: n = 18). Better performance was observed during moderate road traffic noise at 50 dB(A) compared to loud traffic noise at 70 dB(A) in attention-based tasks (Experiments 1-2). This contrasted with the effects of irrelevant speech (60 dB(A)), which was included in the experiments as a well-explored and common noise source in office settings. A disturbance impact of background speech was only given in the two tasks that called for storage functions (Experiments 2-3). In addition to the performance data, subjective annoyance ratings were collected. Consistent with the level effect of road traffic noise found in the performance data, a moderate road traffic noise at 50 dB(A) was perceived as significantly less annoying than a loud road traffic noise at 70 dB(A), which was found, however, independently of the task at hand. Furthermore, the background sound condition with the highest detrimental performance effect in a task was also rated as most annoying in this task, i.e., traffic noise at 70 dB(A) in the Stroop task, and background speech in the mental arithmetic and serial recall tasks. PMID:25913554

Integrating Clarus data in traffic signal system operation : a survivable real-time weather-responsive system.

DOT National Transportation Integrated Search

2011-07-11

This report presents a prototype of a secure, dependable, real-time weather-responsive traffic signal system. The prototype executes two tasks: 1) accesses weather information that provides near real-time atmospheric and pavement surface condition ob...

Cockpit displayed traffic information and distributed management in air traffic control

NASA Technical Reports Server (NTRS)

Kreifeldt, J. G.

1980-01-01

A graphical display of information (such as surrounding aircraft and navigation routes) in the cockpit on a cathode ray tube has been proposed for improving the safety, orderliness, and expeditiousness of the air traffic control system. An investigation of this method at NASA-Ames indicated a large reduction in controller verbal work load without increasing pilot verbal load; the visual work may be increased. The cockpit displayed traffic and navigation information system reduced response delays permitting pilots to maintain their spacing more closely and precisely than when depending entirely on controller-issued radar vectors and speed command.

A microcomputer based traffic evacuation modeling system for emergency planning application

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rathi, A.K.

1994-12-01

Vehicular evacuation is one of the major and often preferred protective action options available for emergency management in a real or anticipated disaster. Computer simulation models of evacuation traffic flow are used to estimate the time required for the affected populations to evacuate to safer areas, to evaluate effectiveness of vehicular evacuations as a protective action option. and to develop comprehensive evacuation plans when required. Following a review of the past efforts to simulate traffic flow during emergency evacuations, an overview of the key features in Version 2.0 of the Oak Ridge Evacuation Modeling System (OREMS) are presented in thismore » paper. OREMS is a microcomputer-based model developed to simulate traffic flow during regional emergency evacuations. OREMS integrates a state-of-the-art dynamic traffic flow and simulation model with advanced data editing and output display programs operating under a MS-Windows environment.« less

Highway traffic estimation of improved precision using the derivative-free nonlinear Kalman Filter

NASA Astrophysics Data System (ADS)

Rigatos, Gerasimos; Siano, Pierluigi; Zervos, Nikolaos; Melkikh, Alexey

2015-12-01

The paper proves that the PDE dynamic model of the highway traffic is a differentially flat one and by applying spatial discretization its shows that the model's transformation into an equivalent linear canonical state-space form is possible. For the latter representation of the traffic's dynamics, state estimation is performed with the use of the Derivative-free nonlinear Kalman Filter. The proposed filter consists of the Kalman Filter recursion applied on the transformed state-space model of the highway traffic. Moreover, it makes use of an inverse transformation, based again on differential flatness theory which enables to obtain estimates of the state variables of the initial nonlinear PDE model. By avoiding approximate linearizations and the truncation of nonlinear terms from the PDE model of the traffic's dynamics the proposed filtering methods outperforms, in terms of accuracy, other nonlinear estimators such as the Extended Kalman Filter. The article's theoretical findings are confirmed through simulation experiments.

Childhood incident asthma and traffic-related air pollution at home and school.

PubMed

McConnell, Rob; Islam, Talat; Shankardass, Ketan; Jerrett, Michael; Lurmann, Fred; Gilliland, Frank; Gauderman, Jim; Avol, Ed; Künzli, Nino; Yao, Ling; Peters, John; Berhane, Kiros

2010-07-01

Traffic-related air pollution has been associated with adverse cardiorespiratory effects, including increased asthma prevalence. However, there has been little study of effects of traffic exposure at school on new-onset asthma. We evaluated the relationship of new-onset asthma with traffic-related pollution near homes and schools. Parent-reported physician diagnosis of new-onset asthma (n = 120) was identified during 3 years of follow-up of a cohort of 2,497 kindergarten and first-grade children who were asthma- and wheezing-free at study entry into the Southern California Children's Health Study. We assessed traffic-related pollution exposure based on a line source dispersion model of traffic volume, distance from home and school, and local meteorology. Regional ambient ozone, nitrogen dioxide (NO(2)), and particulate matter were measured continuously at one central site monitor in each of 13 study communities. Hazard ratios (HRs) for new-onset asthma were scaled to the range of ambient central site pollutants and to the residential interquartile range for each traffic exposure metric. Asthma risk increased with modeled traffic-related pollution exposure from roadways near homes [HR 1.51; 95% confidence interval (CI), 1.25-1.82] and near schools (HR 1.45; 95% CI, 1.06-1.98). Ambient NO(2) measured at a central site in each community was also associated with increased risk (HR 2.18; 95% CI, 1.18-4.01). In models with both NO(2) and modeled traffic exposures, there were independent associations of asthma with traffic-related pollution at school and home, whereas the estimate for NO(2) was attenuated (HR 1.37; 95% CI, 0.69-2.71). Traffic-related pollution exposure at school and homes may both contribute to the development of asthma.

An extended car-following model considering the appearing probability of truck and driver's characteristics

NASA Astrophysics Data System (ADS)

Rong, Ying; Wen, Huiying

2018-05-01

In this paper, the appearing probability of truck is introduced and an extended car-following model is presented to analyze the traffic flow based on the consideration of driver's characteristics, under honk environment. The stability condition of this proposed model is obtained through linear stability analysis. In order to study the evolution properties of traffic wave near the critical point, the mKdV equation is derived by the reductive perturbation method. The results show that the traffic flow will become more disorder for the larger appearing probability of truck. Besides, the appearance of leading truck affects not only the stability of traffic flow, but also the effect of other aspects on traffic flow, such as: driver's reaction and honk effect. The effects of them on traffic flow are closely correlated with the appearing probability of truck. Finally, the numerical simulations under the periodic boundary condition are carried out to verify the proposed model. And they are consistent with the theoretical findings.

The development and evaluation of accident predictive models

NASA Astrophysics Data System (ADS)

Maleck, T. L.

1980-12-01

A mathematical model that will predict the incremental change in the dependent variables (accident types) resulting from changes in the independent variables is developed. The end product is a tool for estimating the expected number and type of accidents for a given highway segment. The data segments (accidents) are separated in exclusive groups via a branching process and variance is further reduced using stepwise multiple regression. The standard error of the estimate is calculated for each model. The dependent variables are the frequency, density, and rate of 18 types of accidents among the independent variables are: district, county, highway geometry, land use, type of zone, speed limit, signal code, type of intersection, number of intersection legs, number of turn lanes, left-turn control, all-red interval, average daily traffic, and outlier code. Models for nonintersectional accidents did not fit nor validate as well as models for intersectional accidents.

Advanced Traffic Management Systems (ATMS) research analysis database system

DOT National Transportation Integrated Search

2001-06-01

The ATMS Research Analysis Database Systems (ARADS) consists of a Traffic Software Data Dictionary (TSDD) and a Traffic Software Object Model (TSOM) for application to microscopic traffic simulation and signal optimization domains. The purpose of thi...

Community structure in traffic zones based on travel demand

NASA Astrophysics Data System (ADS)

Sun, Li; Ling, Ximan; He, Kun; Tan, Qian

2016-09-01

Large structure in complex networks can be studied by dividing it into communities or modules. Urban traffic system is one of the most critical infrastructures. It can be abstracted into a complex network composed of tightly connected groups. Here, we analyze community structure in urban traffic zones based on the community detection method in network science. Spectral algorithm using the eigenvectors of matrices is employed. Our empirical results indicate that the traffic communities are variant with the travel demand distribution, since in the morning the majority of the passengers are traveling from home to work and in the evening they are traveling a contrary direction. Meanwhile, the origin-destination pairs with large number of trips play a significant role in urban traffic network's community division. The layout of traffic community in a city also depends on the residents' trajectories.

Key Technology of Real-Time Road Navigation Method Based on Intelligent Data Research

PubMed Central

Tang, Haijing; Liang, Yu; Huang, Zhongnan; Wang, Taoyi; He, Lin; Du, Yicong; Ding, Gangyi

2016-01-01

The effect of traffic flow prediction plays an important role in routing selection. Traditional traffic flow forecasting methods mainly include linear, nonlinear, neural network, and Time Series Analysis method. However, all of them have some shortcomings. This paper analyzes the existing algorithms on traffic flow prediction and characteristics of city traffic flow and proposes a road traffic flow prediction method based on transfer probability. This method first analyzes the transfer probability of upstream of the target road and then makes the prediction of the traffic flow at the next time by using the traffic flow equation. Newton Interior-Point Method is used to obtain the optimal value of parameters. Finally, it uses the proposed model to predict the traffic flow at the next time. By comparing the existing prediction methods, the proposed model has proven to have good performance. It can fast get the optimal value of parameters faster and has higher prediction accuracy, which can be used to make real-time traffic flow prediction. PMID:27872637

The performance evaluation of a new neural network based traffic management scheme for a satellite communication network

NASA Technical Reports Server (NTRS)

Ansari, Nirwan; Liu, Dequan

1991-01-01

A neural-network-based traffic management scheme for a satellite communication network is described. The scheme consists of two levels of management. The front end of the scheme is a derivation of Kohonen's self-organization model to configure maps for the satellite communication network dynamically. The model consists of three stages. The first stage is the pattern recognition task, in which an exemplar map that best meets the current network requirements is selected. The second stage is the analysis of the discrepancy between the chosen exemplar map and the state of the network, and the adaptive modification of the chosen exemplar map to conform closely to the network requirement (input data pattern) by means of Kohonen's self-organization. On the basis of certain performance criteria, whether a new map is generated to replace the original chosen map is decided in the third stage. A state-dependent routing algorithm, which arranges the incoming call to some proper path, is used to make the network more efficient and to lower the call block rate. Simulation results demonstrate that the scheme, which combines self-organization and the state-dependent routing mechanism, provides better performance in terms of call block rate than schemes that only have either the self-organization mechanism or the routing mechanism.

Multilane Traffic Flow Modeling Using Cellular Automata Theory

NASA Astrophysics Data System (ADS)

Chechina, Antonina; Churbanova, Natalia; Trapeznikova, Marina

2018-02-01

The paper deals with the mathematical modeling of traffic flows on urban road networks using microscopic approach. The model is based on the cellular automata theory and presents a generalization of the Nagel-Schreckenberg model to a multilane case. The created program package allows to simulate traffic on various types of road fragments (T or X type intersection, strait road elements, etc.) and on road networks that consist of these elements. Besides that, it allows to predict the consequences of various decisions regarding road infrastructure changes, such as: number of lanes increasing/decreasing, putting new traffic lights into operation, building new roads, entrances/exits, road junctions.

Traffic Behavior Recognition Using the Pachinko Allocation Model

PubMed Central

Huynh-The, Thien; Banos, Oresti; Le, Ba-Vui; Bui, Dinh-Mao; Yoon, Yongik; Lee, Sungyoung

2015-01-01

CCTV-based behavior recognition systems have gained considerable attention in recent years in the transportation surveillance domain for identifying unusual patterns, such as traffic jams, accidents, dangerous driving and other abnormal behaviors. In this paper, a novel approach for traffic behavior modeling is presented for video-based road surveillance. The proposed system combines the pachinko allocation model (PAM) and support vector machine (SVM) for a hierarchical representation and identification of traffic behavior. A background subtraction technique using Gaussian mixture models (GMMs) and an object tracking mechanism based on Kalman filters are utilized to firstly construct the object trajectories. Then, the sparse features comprising the locations and directions of the moving objects are modeled by PAM into traffic topics, namely activities and behaviors. As a key innovation, PAM captures not only the correlation among the activities, but also among the behaviors based on the arbitrary directed acyclic graph (DAG). The SVM classifier is then utilized on top to train and recognize the traffic activity and behavior. The proposed model shows more flexibility and greater expressive power than the commonly-used latent Dirichlet allocation (LDA) approach, leading to a higher recognition accuracy in the behavior classification. PMID:26151213

23 CFR 772.17 - Traffic noise prediction.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 23 Highways 1 2011-04-01 2011-04-01 false Traffic noise prediction. 772.17 Section 772.17 Highways... ABATEMENT OF HIGHWAY TRAFFIC NOISE AND CONSTRUCTION NOISE § 772.17 Traffic noise prediction. (a) Any analysis required by this subpart must use the FHWA Traffic Noise Model (FHWA TNM), which is described in...

Modeling hurricane evacuation traffic : a mobile real-time traffic counter for monitoring hurricane evacuation traffic conditions.

DOT National Transportation Integrated Search

2006-04-01

The objective of this part of the research study was to select and acquire a mobile traffic counter capable of providing traffic flow and average speed data in intervals of time no greater than 15 minutes and transmit the data back to a central locat...

Studies of vehicle lane-changing dynamics and its effect on traffic efficiency, safety and environmental impact

NASA Astrophysics Data System (ADS)

Li, Xiang; Sun, Jian-Qiao

2017-02-01

Drivers often change lanes on the road to maintain desired speed and to avoid slow vehicles, pedestrians, obstacles and lane closure. Understanding the effect of lane-changing on the traffic is an important topic in designing optimal traffic control systems. This paper presents a comprehensive study of this topic. We review the theory of microscopic dynamic car-following models and the lane-changing models, propose additional lane-changing rules to deal with moving bottleneck and lane reduction, and investigate the effects of lane-changing on the traffic efficiency, traffic safety and fuel consumption as a function of different variables including the distance of the emergency sign ahead of the lane closure, speed limit, traffic density, etc. Extensive simulations of the traffic system have been carried out in different scenarios. A number of important findings of the effect of various factors on the traffic are reported. These findings provide guidance on the traffic management and are important to the designers and engineers of modern highway or inner city roads to achieve high traffic efficiency and safety with minimum environmental impact.

Value of Information for Optimal Adaptive Routing in Stochastic Time-Dependent Traffic Networks: Algorithms and Computational Tools

DOT National Transportation Integrated Search

2010-10-25

Real-time information is important for travelers' routing decisions in uncertain networks by enabling online adaptation to revealed traffic conditions. Usually there are spatial and/or temporal limitations in traveler information. In this research, a...

Capacity-constrained traffic assignment in networks with residual queues

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lam, W.H.K.; Zhang, Y.

2000-04-01

This paper proposes a capacity-constrained traffic assignment model for strategic transport planning in which the steady-state user equilibrium principle is extended for road networks with residual queues. Therefore, the road-exit capacity and the queuing effects can be incorporated into the strategic transport model for traffic forecasting. The proposed model is applicable to the congested network particularly when the traffic demands exceeds the capacity of the network during the peak period. An efficient solution method is proposed for solving the steady-state traffic assignment problem with residual queues. Then a simple numerical example is employed to demonstrate the application of the proposedmore » model and solution method, while an example of a medium-sized arterial highway network in Sioux Falls, South Dakota, is used to test the applicability of the proposed solution to real problems.« less

Design of an air traffic computer simulation system to support investigation of civil tiltrotor aircraft operations

NASA Technical Reports Server (NTRS)

Rogers, Ralph V.

1992-01-01

This research project addresses the need to provide an efficient and safe mechanism to investigate the effects and requirements of the tiltrotor aircraft's commercial operations on air transportation infrastructures, particularly air traffic control. The mechanism of choice is computer simulation. Unfortunately, the fundamental paradigms of the current air traffic control simulation models do not directly support the broad range of operational options and environments necessary to study tiltrotor operations. Modification of current air traffic simulation models to meet these requirements does not appear viable given the range and complexity of issues needing resolution. As a result, the investigation of systemic, infrastructure issues surrounding the effects of tiltrotor commercial operations requires new approaches to simulation modeling. These models should be based on perspectives and ideas closer to those associated with tiltrotor air traffic operations.

Modeling the Environmental Impact of Air Traffic Operations

NASA Technical Reports Server (NTRS)

Chen, Neil

2011-01-01

There is increased interest to understand and mitigate the impacts of air traffic on the climate, since greenhouse gases, nitrogen oxides, and contrails generated by air traffic can have adverse impacts on the climate. The models described in this presentation are useful for quantifying these impacts and for studying alternative environmentally aware operational concepts. These models have been developed by leveraging and building upon existing simulation and optimization techniques developed for the design of efficient traffic flow management strategies. Specific enhancements to the existing simulation and optimization techniques include new models that simulate aircraft fuel flow, emissions and contrails. To ensure that these new models are beneficial to the larger climate research community, the outputs of these new models are compatible with existing global climate modeling tools like the FAA's Aviation Environmental Design Tool.

dOCRL maintains immune cell quiescence by regulating endosomal traffic

PubMed Central

Del Signore, Steven J.; Biber, Sarah A.; Lehmann, Katherine S.; Heimler, Stephanie R.; Rosenfeld, Benjamin H.; Eskin, Tania L.

2017-01-01

Lowe Syndrome is a developmental disorder characterized by eye, kidney, and neurological pathologies, and is caused by mutations in the phosphatidylinositol-5-phosphatase OCRL. OCRL plays diverse roles in endocytic and endolysosomal trafficking, cytokinesis, and ciliogenesis, but it is unclear which of these cellular functions underlie specific patient symptoms. Here, we show that mutation of Drosophila OCRL causes cell-autonomous activation of hemocytes, which are macrophage-like cells of the innate immune system. Among many cell biological defects that we identified in docrl mutant hemocytes, we pinpointed the cause of innate immune cell activation to reduced Rab11-dependent recycling traffic and concomitantly increased Rab7-dependent late endosome traffic. Loss of docrl amplifies multiple immune-relevant signals, including Toll, Jun kinase, and STAT, and leads to Rab11-sensitive mis-sorting and excessive secretion of the Toll ligand Spåtzle. Thus, docrl regulation of endosomal traffic maintains hemocytes in a poised, but quiescent state, suggesting mechanisms by which endosomal misregulation of signaling may contribute to symptoms of Lowe syndrome. PMID:29028801

The effects of meaningful irrelevant speech and road traffic noise on teachers' attention, episodic and semantic memory.

PubMed

Enmarker, Ingela

2004-11-01

The aim of the present experiment was to examine the effects of meaningful irrelevant speech and road traffic noise on attention, episodic and semantic memory, and also to examine whether the noise effects were age-dependent. A total of 96 male and female teachers in the age range of 35-45 and 55-65 years were randomly assigned to a silent or the two noise conditions. Noise effects found in episodic memory were limited to a meaningful text, where cued recall contrary to expectations was equally impaired by the two types of noise. However, meaningful irrelevant speech also deteriorated recognition of the text, whereas road traffic noise caused no decrement. Retrieval from two word fluency tests in semantic memory showed strong effects of noise exposure, one affected by meaningful irrelevant speech and the other by road traffic noise. The results implied that both acoustic variation and the semantic interference could be of importance for noise impairments. The expected age-dependent noise effects did not show up.

Using Tensor Completion Method to Achieving Better Coverage of Traffic State Estimation from Sparse Floating Car Data

PubMed Central

Ran, Bin; Song, Li; Cheng, Yang; Tan, Huachun

2016-01-01

Traffic state estimation from the floating car system is a challenging problem. The low penetration rate and random distribution make available floating car samples usually cover part space and time points of the road networks. To obtain a wide range of traffic state from the floating car system, many methods have been proposed to estimate the traffic state for the uncovered links. However, these methods cannot provide traffic state of the entire road networks. In this paper, the traffic state estimation is transformed to solve a missing data imputation problem, and the tensor completion framework is proposed to estimate missing traffic state. A tensor is constructed to model traffic state in which observed entries are directly derived from floating car system and unobserved traffic states are modeled as missing entries of constructed tensor. The constructed traffic state tensor can represent spatial and temporal correlations of traffic data and encode the multi-way properties of traffic state. The advantage of the proposed approach is that it can fully mine and utilize the multi-dimensional inherent correlations of traffic state. We tested the proposed approach on a well calibrated simulation network. Experimental results demonstrated that the proposed approach yield reliable traffic state estimation from very sparse floating car data, particularly when dealing with the floating car penetration rate is below 1%. PMID:27448326

Using Tensor Completion Method to Achieving Better Coverage of Traffic State Estimation from Sparse Floating Car Data.

PubMed

Ran, Bin; Song, Li; Zhang, Jian; Cheng, Yang; Tan, Huachun

2016-01-01

Traffic state estimation from the floating car system is a challenging problem. The low penetration rate and random distribution make available floating car samples usually cover part space and time points of the road networks. To obtain a wide range of traffic state from the floating car system, many methods have been proposed to estimate the traffic state for the uncovered links. However, these methods cannot provide traffic state of the entire road networks. In this paper, the traffic state estimation is transformed to solve a missing data imputation problem, and the tensor completion framework is proposed to estimate missing traffic state. A tensor is constructed to model traffic state in which observed entries are directly derived from floating car system and unobserved traffic states are modeled as missing entries of constructed tensor. The constructed traffic state tensor can represent spatial and temporal correlations of traffic data and encode the multi-way properties of traffic state. The advantage of the proposed approach is that it can fully mine and utilize the multi-dimensional inherent correlations of traffic state. We tested the proposed approach on a well calibrated simulation network. Experimental results demonstrated that the proposed approach yield reliable traffic state estimation from very sparse floating car data, particularly when dealing with the floating car penetration rate is below 1%.

Density waves in granular flow

NASA Astrophysics Data System (ADS)

Herrmann, H. J.; Flekkøy, E.; Nagel, K.; Peng, G.; Ristow, G.

Ample experimental evidence has shown the existence of spontaneous density waves in granular material flowing through pipes or hoppers. Using Molecular Dynamics Simulations we show that several types of waves exist and find that these density fluctuations follow a 1/f spectrum. We compare this behaviour to deterministic one-dimensional traffic models. If positions and velocities are continuous variables the model shows self-organized criticality driven by the slowest car. We also present Lattice Gas and Boltzmann Lattice Models which reproduce the experimentally observed effects. Density waves are spontaneously generated when the viscosity has a nonlinear dependence on density which characterizes granular flow.

Traffic evacuation time under nonhomogeneous conditions.

PubMed

Fazio, Joseph; Shetkar, Rohan; Mathew, Tom V

2017-06-01

During many manmade and natural crises such as terrorist threats, floods, hazardous chemical and gas leaks, emergency personnel need to estimate the time in which people can evacuate from the affected urban area. Knowing an estimated evacuation time for a given crisis, emergency personnel can plan and prepare accordingly with the understanding that the actual evacuation time will take longer. Given the urban area to be evacuated, street widths exiting the area's perimeter, the area's population density, average vehicle occupancy, transport mode share and crawl speed, an estimation of traffic evacuation time can be derived. Peak-hour traffic data collected at three, midblock, Mumbai sites of varying geometric features and traffic composition were used in calibrating a model that estimates peak-hour traffic flow rates. Model validation revealed a correlation coefficient of +0.98 between observed and predicted peak-hour flow rates. A methodology is developed that estimates traffic evacuation time using the model.

Reducing Traffic Congestions by Introducing CACC-Vehicles on a Multi-Lane Highway Using Agent-Based Approach

NASA Technical Reports Server (NTRS)

Arnaout, Georges M.; Bowling, Shannon R.

2011-01-01

Traffic congestion is an ongoing problem of great interest to researchers from different areas in academia. With the emerging technology for inter-vehicle communication, vehicles have the ability to exchange information with predecessors by wireless communication. In this paper, we present an agent-based model of traffic congestion and examine the impact of having CACC (Cooperative Adaptive Cruise Control) embedded vehicle(s) on a highway system consisting of 4 traffic lanes without overtaking. In our model, CACC vehicles adapt their acceleration/deceleration according to vehicle-to-vehicle inter-communication. We analyze the average speed of the cars, the shockwaves, and the evolution of traffic congestion throughout the lifecycle of the model. The study identifies how CACC vehicles affect the dynamics of traffic flow on a complex network and reduce the oscillatory behavior (stop and go) resulting from the acceleration/deceleration of the vehicles.

Modeling level-of-safety for bus stops in China.

PubMed

Ye, Zhirui; Wang, Chao; Yu, Yongbo; Shi, Xiaomeng; Wang, Wei

2016-08-17

Safety performance at bus stops is generally evaluated by using historical traffic crash data or traffic conflict data. However, in China, it is quite difficult to obtain such data mainly due to the lack of traffic data management and organizational issues. In light of this, the primary objective of this study is to develop a quantitative approach to evaluate bus stop safety performance. The concept of level-of-safety for bus stops is introduced and corresponding models are proposed to quantify safety levels, which consider conflict points, traffic factors, geometric characteristics, traffic signs and markings, pavement conditions, and lighting conditions. Principal component analysis and k-means clustering methods were used to model and quantify safety levels for bus stops. A case study was conducted to show the applicability of the proposed model with data collected from 46 samples for the 7 most common types of bus stops in China, using 32 of the samples for modeling and 14 samples for illustration. Based on the case study, 6 levels of safety for bus stops were defined. Finally, a linear regression analysis between safety levels and the number of traffic conflicts showed that they had a strong relationship (R(2) value of 0.908). The results indicated that the method was well validated and could be practically used for the analysis and evaluation of bus stop safety in China. The proposed model was relatively easy to implement without the requirement of traffic crash data and/or traffic conflict data. In addition, with the proposed method, it was feasible to evaluate countermeasures to improve bus stop safety (e.g., exclusive bus lanes).

Complex motion of a vehicle through a series of signals controlled by power-law phase

NASA Astrophysics Data System (ADS)

Nagatani, Takashi

2017-07-01

We study the dynamic motion of a vehicle moving through the series of traffic signals controlled by the position-dependent phase of power law. All signals are controlled by both cycle time and position-dependent phase. The dynamic model of the vehicular motion is described in terms of the nonlinear map. The vehicular motion varies in a complex manner by varying cycle time for various values of the power of the position-dependent phase. The vehicle displays the periodic motion with a long cycle for the integer power of the phase, while the vehicular motion exhibits the very complex behavior for the non-integer power of the phase.

On sequential data assimilation for scalar macroscopic traffic flow models

NASA Astrophysics Data System (ADS)

Blandin, Sébastien; Couque, Adrien; Bayen, Alexandre; Work, Daniel

2012-09-01

We consider the problem of sequential data assimilation for transportation networks using optimal filtering with a scalar macroscopic traffic flow model. Properties of the distribution of the uncertainty on the true state related to the specific nonlinearity and non-differentiability inherent to macroscopic traffic flow models are investigated, derived analytically and analyzed. We show that nonlinear dynamics, by creating discontinuities in the traffic state, affect the performances of classical filters and in particular that the distribution of the uncertainty on the traffic state at shock waves is a mixture distribution. The non-differentiability of traffic dynamics around stationary shock waves is also proved and the resulting optimality loss of the estimates is quantified numerically. The properties of the estimates are explicitly studied for the Godunov scheme (and thus the Cell-Transmission Model), leading to specific conclusions about their use in the context of filtering, which is a significant contribution of this article. Analytical proofs and numerical tests are introduced to support the results presented. A Java implementation of the classical filters used in this work is available on-line at http://traffic.berkeley.edu for facilitating further efforts on this topic and fostering reproducible research.

Particles from wood smoke and traffic induce differential pro-inflammatory response patterns in co-cultures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kocbach, Anette; Herseth, Jan Inge; Lag, Marit

2008-10-15

The inflammatory potential of particles from wood smoke and traffic has not been well elucidated. In this study, a contact co-culture of monocytes and pneumocytes was exposed to 10-40 {mu}g/cm{sup 2} of particles from wood smoke and traffic for 12, 40 and 64 h to determine their influence on pro-inflammatory cytokine release (TNF-{alpha}, IL-1, IL-6, IL-8) and viability. To investigate the role of organic constituents in cytokine release the response to particles, their organic extracts and the washed particles were compared. Antagonists were used to investigate source-dependent differences in intercellular signalling (TNF-{alpha}, IL-1). The cytotoxicity was low after exposure tomore » particles from both sources. However, wood smoke, and to a lesser degree traffic-derived particles, induced a reduction in cell number, which was associated with the organic fraction. The release of pro-inflammatory cytokines was similar for both sources after 12 h, but traffic induced a greater release than wood smoke particles with increasing exposure time. The organic fraction accounted for the majority of the cytokine release induced by wood smoke, whereas the washed traffic particles induced a stronger response than the corresponding organic extract. TNF-{alpha} and IL-1 antagonists reduced the release of IL-8 induced by particles from both sources. In contrast, the IL-6 release was only reduced by the IL-1 antagonist during exposure to traffic-derived particles. In summary, particles from wood smoke and traffic induced differential pro-inflammatory response patterns with respect to cytokine release and cell number. Moreover, the influence of the organic particle fraction and intercellular signalling on the pro-inflammatory response seemed to be source-dependent.« less

High-resolution numerical approximation of traffic flow problems with variable lanes and free-flow velocities.

PubMed

Zhang, Peng; Liu, Ru-Xun; Wong, S C

2005-05-01

This paper develops macroscopic traffic flow models for a highway section with variable lanes and free-flow velocities, that involve spatially varying flux functions. To address this complex physical property, we develop a Riemann solver that derives the exact flux values at the interface of the Riemann problem. Based on this solver, we formulate Godunov-type numerical schemes to solve the traffic flow models. Numerical examples that simulate the traffic flow around a bottleneck that arises from a drop in traffic capacity on the highway section are given to illustrate the efficiency of these schemes.

Traffic-related air pollution and obesity formation in children: a longitudinal, multilevel analysis.

PubMed

Jerrett, Michael; McConnell, Rob; Wolch, Jennifer; Chang, Roger; Lam, Claudia; Dunton, Genevieve; Gilliland, Frank; Lurmann, Fred; Islam, Talat; Berhane, Kiros

2014-06-09

Biologically plausible mechanisms link traffic-related air pollution to metabolic disorders and potentially to obesity. Here we sought to determine whether traffic density and traffic-related air pollution were positively associated with growth in body mass index (BMI = kg/m2) in children aged 5-11 years. Participants were drawn from a prospective cohort of children who lived in 13 communities across Southern California (N = 4550). Children were enrolled while attending kindergarten and first grade and followed for 4 years, with height and weight measured annually. Dispersion models were used to estimate exposure to traffic-related air pollution. Multilevel models were used to estimate and test traffic density and traffic pollution related to BMI growth. Data were collected between 2002-2010 and analyzed in 2011-12. Traffic pollution was positively associated with growth in BMI and was robust to adjustment for many confounders. The effect size in the adjusted model indicated about a 13.6% increase in annual BMI growth when comparing the lowest to the highest tenth percentile of air pollution exposure, which resulted in an increase of nearly 0.4 BMI units on attained BMI at age 10. Traffic density also had a positive association with BMI growth, but this effect was less robust in multivariate models. Traffic pollution was positively associated with growth in BMI in children aged 5-11 years. Traffic pollution may be controlled via emission restrictions; changes in land use that promote jobs-housing balance and use of public transit and hence reduce vehicle miles traveled; promotion of zero emissions vehicles; transit and car-sharing programs; or by limiting high pollution traffic, such as diesel trucks, from residential areas or places where children play outdoors, such as schools and parks. These measures may have beneficial effects in terms of reduced obesity formation in children.

Traffic-related air pollution and obesity formation in children: a longitudinal, multilevel analysis

PubMed Central

2014-01-01

Background Biologically plausible mechanisms link traffic-related air pollution to metabolic disorders and potentially to obesity. Here we sought to determine whether traffic density and traffic-related air pollution were positively associated with growth in body mass index (BMI = kg/m2) in children aged 5–11 years. Methods Participants were drawn from a prospective cohort of children who lived in 13 communities across Southern California (N = 4550). Children were enrolled while attending kindergarten and first grade and followed for 4 years, with height and weight measured annually. Dispersion models were used to estimate exposure to traffic-related air pollution. Multilevel models were used to estimate and test traffic density and traffic pollution related to BMI growth. Data were collected between 2002–2010 and analyzed in 2011–12. Results Traffic pollution was positively associated with growth in BMI and was robust to adjustment for many confounders. The effect size in the adjusted model indicated about a 13.6% increase in annual BMI growth when comparing the lowest to the highest tenth percentile of air pollution exposure, which resulted in an increase of nearly 0.4 BMI units on attained BMI at age 10. Traffic density also had a positive association with BMI growth, but this effect was less robust in multivariate models. Conclusions Traffic pollution was positively associated with growth in BMI in children aged 5–11 years. Traffic pollution may be controlled via emission restrictions; changes in land use that promote jobs-housing balance and use of public transit and hence reduce vehicle miles traveled; promotion of zero emissions vehicles; transit and car-sharing programs; or by limiting high pollution traffic, such as diesel trucks, from residential areas or places where children play outdoors, such as schools and parks. These measures may have beneficial effects in terms of reduced obesity formation in children. PMID:24913018

Analysis of mixed traffic flow with human-driving and autonomous cars based on car-following model

NASA Astrophysics Data System (ADS)

Zhu, Wen-Xing; Zhang, H. M.

2018-04-01

We investigated the mixed traffic flow with human-driving and autonomous cars. A new mathematical model with adjustable sensitivity and smooth factor was proposed to describe the autonomous car's moving behavior in which smooth factor is used to balance the front and back headway in a flow. A lemma and a theorem were proved to support the stability criteria in traffic flow. A series of simulations were carried out to analyze the mixed traffic flow. The fundamental diagrams were obtained from the numerical simulation results. The varying sensitivity and smooth factor of autonomous cars affect traffic flux, which exhibits opposite varying tendency with increasing parameters before and after the critical density. Moreover, the sensitivity of sensors and smooth factors play an important role in stabilizing the mixed traffic flow and suppressing the traffic jam.

Improvement of driving safety in road traffic system

NASA Astrophysics Data System (ADS)

Li, Ke-Ping; Gao, Zi-You

2005-05-01

A road traffic system is a complex system in which humans participate directly. In this system, human factors play a very important role. In this paper, a kind of control signal is designated at a given site (i.e., signal point) of the road. Under the effect of the control signal, the drivers will decrease their velocities when their vehicles pass the signal point. Our aim is to transit the traffic flow states from disorder to order and then improve the traffic safety. We have tested this technique for the two-lane traffic model that is based on the deterministic Nagel-Schreckenberg (NaSch) traffic model. The simulation results indicate that the traffic flow states can be transited from disorder to order. Different order states can be observed in the system and these states are safer.

Does the livability of a residential street depend on the characteristics of the neighboring street network?

DOT National Transportation Integrated Search

2016-07-01

Shortly after the advent of cars, a conflict arose between moving traffic and residential livability. The typical response was to push traffic off residential streets and onto nearby major roads. This line of thinking evolved into a more hierarchical...

Evaluation of functional color vision requirements and current color vision screening tests for air traffic control specialists.

DOT National Transportation Integrated Search

1990-08-01

An experiment was conducted to evaluate the relation of type and degree of color vision deficiency and aeromedical color vision screening test scores to performance of color-dependent tasks of Air Traffic Control Specialists. The subjects included 37...

Design/build vs traditional construction user delay modeling : an evaluation of the cost effectiveness of innovative construction methods for new construction. Part 2 : VISUM Online for Salt Lake, Davis, and Utah Counties

DOT National Transportation Integrated Search

2007-05-01

VISUM Online is a traffic management system for processing online traffic data. The system implements both a road network model and a traffic demand model. VISUM Online uses all available real-time and historic data to calculate current and forecaste...

Strategic Air Traffic Planning Using Eulerian Route Based Modeling and Optimization

NASA Astrophysics Data System (ADS)

Bombelli, Alessandro

Due to a soaring air travel growth in the last decades, air traffic management has become increasingly challenging. As a consequence, planning tools are being devised to help human decision-makers achieve a better management of air traffic. Planning tools are divided into two categories, strategic and tactical. Strategic planning generally addresses a larger planning domain and is performed days to hours in advance. Tactical planning is more localized and is performed hours to minutes in advance. An aggregate route model for strategic air traffic flow management is presented. It is an Eulerian model, describing the flow between cells of unidirectional point-to-point routes. Aggregate routes are created from flight trajectory data based on similarity measures. Spatial similarity is determined using the Frechet distance. The aggregate routes approximate actual well-traveled traffic patterns. By specifying the model resolution, an appropriate balance between model accuracy and model dimension can be achieved. For a particular planning horizon, during which weather is expected to restrict the flow, a procedure for designing airborne reroutes and augmenting the traffic flow model is developed. The dynamics of the traffic flow on the resulting network take the form of a discrete-time, linear time-invariant system. The traffic flow controls are ground holding, pre-departure rerouting and airborne rerouting. Strategic planning--determining how the controls should be used to modify the future traffic flow when local capacity violations are anticipated--is posed as an integer programming problem of minimizing a weighted sum of flight delays subject to control and capacity constraints. Several tests indicate the effectiveness of the modeling and strategic planning approach. In the final, most challenging, test, strategic planning is demonstrated for the six western-most Centers of the 22-Center national airspace. The planning time horizon is four hours long, and there is weather predicted that causes significant delays to the scheduled flights. Airborne reroute options are computed and added to the route model, and it is shown that the predicted delays can be significantly reduced. The test results also indicate the computational feasibility of the approach for a planning problem of this size.

CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: A Realistic Cellular Automaton Model for Synchronized Traffic Flow

NASA Astrophysics Data System (ADS)

Zhao, Bo-Han; Hu, Mao-Bin; Jiang, Rui; Wu, Qing-Song

2009-11-01

A cellular automaton model is proposed to consider the anticipation effect in drivers' behavior. It is shown that the anticipation effect can be one of the origins of synchronized traffic flow. With anticipation effect, the congested traffic flow simulated by the model exhibits the features of synchronized flow. The spatiotemporal patterns induced by an on-ramp are also consistent with the three-phase traffic theory. Since the origin of synchronized flow is still controversial, our work can shed some light on the mechanism of synchronized flow.

An original traffic additional emission model and numerical simulation on a signalized road

NASA Astrophysics Data System (ADS)

Zhu, Wen-Xing; Zhang, Jing-Yu

2017-02-01

Based on VSP (Vehicle Specific Power) model traffic real emissions were theoretically classified into two parts: basic emission and additional emission. An original additional emission model was presented to calculate the vehicle's emission due to the signal control effects. Car-following model was developed and used to describe the traffic behavior including cruising, accelerating, decelerating and idling at a signalized intersection. Simulations were conducted under two situations: single intersection and two adjacent intersections with their respective control policy. Results are in good agreement with the theoretical analysis. It is also proved that additional emission model may be used to design the signal control policy in our modern traffic system to solve the serious environmental problems.

STOL Traffic environment and operational procedures

NASA Technical Reports Server (NTRS)

Schlundt, R. W.; Dewolf, R. W.; Ausrotas, R. A.; Curry, R. E.; Demaio, D.; Keene, D. W.; Speyer, J. L.; Weinreich, M.; Zeldin, S.

1972-01-01

The expected traffic environment for an intercity STOL transportation system is examined, and operational procedures are discussed in order to identify problem areas which impact STOL avionics requirements. Factors considered include: traffic densities, STOL/CTOL/VTOL traffic mix, the expect ATC environment, aircraft noise models and community noise models and community noise impact, flight paths for noise abatement, wind considerations affecting landing, approach and landing considerations, STOLport site selection, runway capacity, and STOL operations at jetports, suburban airports, and separate STOLports.

Modelling of road traffic fatalities in India.

PubMed

Goel, Rahul

2018-03-01

Passenger modes in India include walking, cycling, buses, trains, intermediate public transport modes (IPT) such as three-wheeled auto rickshaws or tuk-tuks, motorised two-wheelers (2W) as well as cars. However, epidemiological studies of traffic crashes in India have been limited in their approach to account for the exposure of these road users. In 2011, for the first time, census in India reported travel distance and mode of travel for workers. A Poisson-lognormal mixture regression model is developed at the state level to explore the relationship of road deaths of all the road users with commute travel distance by different on-road modes. The model controlled for diesel consumption (proxy for freight traffic), length of national highways, proportion of population in urban areas, and built-up population density. The results show that walking, cycling and, interestingly, IPT are associated with lower risk of road deaths, while 2W, car and bus are associated with higher risk. Promotion of IPT has twofold benefits of increasing safety as well as providing a sustainable mode of transport. The mode shift scenarios show that, for similar mode shift across the states, the resulting trends in road deaths are highly dependent on the baseline mode shares. The most worrying trend is the steep growth of death burden resulting from mode shift of walking and cycling to 2W. While the paper illustrates a limited set of mode shift scenarios involving two modes at a time, the model can be applied to assess safety impacts resulting from a more complex set of scenarios. Copyright © 2018 The Author. Published by Elsevier Ltd.. All rights reserved.

Effects of urban sprawl and vehicle miles traveled on traffic fatalities.

PubMed

Yeo, Jiho; Park, Sungjin; Jang, Kitae

2015-01-01

Previous research suggests that urban sprawl increases auto-dependency and that excessive auto use increases the risk of traffic fatalities. This indirect effect of urban sprawl on traffic fatalities is compared to non-vehicle miles traveled (VMT)-related direct effect of sprawl on fatalities. We conducted a path analysis to examine the causal linkages among urban sprawl, VMT, traffic fatalities, income, and fuel cost. The path diagram includes 2 major linkages: the direct relationship between urban sprawl and traffic fatalities and the indirect effect on fatalities through increased VMT in sprawling urban areas. To measure the relative strength of these causal linkages, path coefficients are estimated using data collected nationally from 147 urbanized areas in the United States. Through both direct and indirect paths, urban sprawl is associated with greater numbers of traffic fatalities, but the direct effect of sprawl on fatalities is more influential than the indirect effect. Enhancing traffic safety can be achieved by impeding urban sprawl and encouraging compact development. On the other hand, policy tools reducing VMT may be less effective than anticipated for traffic safety.

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

PubMed Central

Yu, Haiyang; Wu, Zhihai; Wang, Shuqin; Wang, Yunpeng; Ma, Xiaolei

2017-01-01

Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction. PMID:28672867

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks.

PubMed

Yu, Haiyang; Wu, Zhihai; Wang, Shuqin; Wang, Yunpeng; Ma, Xiaolei

2017-06-26

Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

INTEGRATED SPEED ESTIMATION MODEL FOR MULTILANE EXPREESSWAYS

NASA Astrophysics Data System (ADS)

Hong, Sungjoon; Oguchi, Takashi

In this paper, an integrated speed-estimation model is developed based on empirical analyses for the basic sections of intercity multilane expressway un der the uncongested condition. This model enables a speed estimation for each lane at any site under arb itrary highway-alignment, traffic (traffic flow and truck percentage), and rainfall conditions. By combin ing this model and a lane-use model which estimates traffic distribution on the lanes by each vehicle type, it is also possible to es timate an average speed across all the lanes of one direction from a traffic demand by vehicle type under specific highway-alignment and rainfall conditions. This model is exp ected to be a tool for the evaluation of traffic performance for expressways when the performance me asure is travel speed, which is necessary for Performance-Oriented Highway Planning and Design. Regarding the highway-alignment condition, two new estimators, called effective horizo ntal curvature and effective vertical grade, are proposed in this paper which take into account the influence of upstream and downstream alignment conditions. They are applied to the speed-estimation model, and it shows increased accuracy of the estimation.

An extended macro model accounting for acceleration changes with memory and numerical tests

NASA Astrophysics Data System (ADS)

Cheng, Rongjun; Ge, Hongxia; Sun, Fengxin; Wang, Jufeng

2018-09-01

Considering effect of acceleration changes with memory, an improved continuum model of traffic flow is proposed in this paper. By applying the linear stability theory, we derived the new model's linear stability condition. Through nonlinear analysis, the KdV-Burgers equation is derived to describe the propagating behavior of traffic density wave near the neutral stability line. Numerical simulation is carried out to study the extended traffic flow model, which explores how acceleration changes with memory affected each car's velocity, density and fuel consumption and exhaust emissions. Numerical results demonstrate that acceleration changes with memory have significant negative effect on dynamic characteristic of traffic flow. Furthermore, research results verify that the effect of acceleration changes with memory will deteriorate the stability of traffic flow and increase cars' total fuel consumptions and emissions during the whole evolution of small perturbation.

Fuzzy State Transition and Kalman Filter Applied in Short-Term Traffic Flow Forecasting

PubMed Central

Ming-jun, Deng; Shi-ru, Qu

2015-01-01

Traffic flow is widely recognized as an important parameter for road traffic state forecasting. Fuzzy state transform and Kalman filter (KF) have been applied in this field separately. But the studies show that the former method has good performance on the trend forecasting of traffic state variation but always involves several numerical errors. The latter model is good at numerical forecasting but is deficient in the expression of time hysteretically. This paper proposed an approach that combining fuzzy state transform and KF forecasting model. In considering the advantage of the two models, a weight combination model is proposed. The minimum of the sum forecasting error squared is regarded as a goal in optimizing the combined weight dynamically. Real detection data are used to test the efficiency. Results indicate that the method has a good performance in terms of short-term traffic forecasting. PMID:26779258

Fuzzy State Transition and Kalman Filter Applied in Short-Term Traffic Flow Forecasting.

PubMed

Deng, Ming-jun; Qu, Shi-ru

2015-01-01

Traffic flow is widely recognized as an important parameter for road traffic state forecasting. Fuzzy state transform and Kalman filter (KF) have been applied in this field separately. But the studies show that the former method has good performance on the trend forecasting of traffic state variation but always involves several numerical errors. The latter model is good at numerical forecasting but is deficient in the expression of time hysteretically. This paper proposed an approach that combining fuzzy state transform and KF forecasting model. In considering the advantage of the two models, a weight combination model is proposed. The minimum of the sum forecasting error squared is regarded as a goal in optimizing the combined weight dynamically. Real detection data are used to test the efficiency. Results indicate that the method has a good performance in terms of short-term traffic forecasting.

Traffic jams induced by fluctuation of a leading car.

PubMed

Nagatani, T

2000-04-01

We present a phase diagram of the different kinds of congested traffic triggered by fluctuation of a leading car in an open system without sources and sinks. Traffic states and density waves are investigated numerically by varying the amplitude of fluctuation using a car following model. The phase transitions among the free traffic, oscillatory congested traffic, and homogeneous congested traffic occur by fluctuation of a leading car. With increasing the amplitude of fluctuation, the transition between the free traffic and oscillatory traffic occurs at lower density and the transition between the homogeneous congested traffic and the oscillatory traffic occurs at higher density. The oscillatory congested traffic corresponds to the coexisting phase. Also, the moving localized clusters appear just above the transition lines.

Disordered cellular automaton traffic flow model: phase separated state, density waves and self organized criticality

NASA Astrophysics Data System (ADS)

Fourrate, K.; Loulidi, M.

2006-01-01

We suggest a disordered traffic flow model that captures many features of traffic flow. It is an extension of the Nagel-Schreckenberg (NaSch) stochastic cellular automata for single line vehicular traffic model. It incorporates random acceleration and deceleration terms that may be greater than one unit. Our model leads under its intrinsic dynamics, for high values of braking probability pr, to a constant flow at intermediate densities without introducing any spatial inhomogeneities. For a system of fast drivers pr→0, the model exhibits a density wave behavior that was observed in car following models with optimal velocity. The gap of the disordered model we present exhibits, for high values of pr and random deceleration, at a critical density, a power law distribution which is a hall mark of a self organized criticality phenomena.

MMPP Traffic Generator for the Testing of the SCAR 2 Fast Packet Switch

NASA Technical Reports Server (NTRS)

Chren, William A., Jr.

1995-01-01

A prototype MWP Traffic Generator (TG) has been designed for testing of the COMSAT-supplied SCAR II Fast Packet Switch. By generating packets distributed according to a Markov-Modulated Poisson Process (MMPP) model. it allows the assessment of the switch performance under traffic conditions that are more realistic than could be generated using the COMSAT-supplied Traffic Generator Module. The MMPP model is widely believed to model accurately real-world superimposed voice and data communications traffic. The TG was designed to be as much as possible of a "drop-in" replacement for the COMSAT Traffic Generator Module. The latter fit on two Altera EPM7256EGC 192-pin CPLDs and produced traffic for one switch input port. No board changes are necessary because it has been partitioned to use the existing board traces. The TG, consisting of parts "TGDATPROC" and "TGRAMCTL" must merely be reprogrammed into the Altera devices of the same name. However, the 040 controller software must be modified to provide TG initialization data. This data will be given in Section II.

Developing a Measure of Traffic Calming Associated with Elementary School Students’ Active Transport

PubMed Central

Nicholson, Lisa M.; Turner, Lindsey; Slater, Sandy J.; Abuzayd, Haytham; Chriqui, Jamie F.; Chaloupka, Frank

2014-01-01

The objective of this study is to develop a measure of traffic calming with nationally available GIS data from NAVTEQ and to validate the traffic calming index with the percentage of children reported by school administrators as walking or biking to school, using data from a nationally representative sample of elementary schools in 2006-2010. Specific models, with and without correlated errors, examined associations of objective GIS measures of the built environment, nationally available from NAVTEQ, with the latent construct of traffic calming. The best fit model for the latent traffic calming construct was determined to be a five factor model including objective measures of intersection density, count of medians/dividers, count of low mobility streets, count of roundabouts, and count of on-street parking availability, with no correlated errors among items. This construct also proved to be a good fit for the full measurement model when the outcome measure of percentage of students walking or biking to school was added to the model. The traffic calming measure was strongly, significantly, and positively correlated with the percentage of students reported as walking or biking to school. Applicability of results to public health and transportation policies and practices are discussed. PMID:25506255

Developing a Measure of Traffic Calming Associated with Elementary School Students' Active Transport.

PubMed

Nicholson, Lisa M; Turner, Lindsey; Slater, Sandy J; Abuzayd, Haytham; Chriqui, Jamie F; Chaloupka, Frank

2014-12-01

The objective of this study is to develop a measure of traffic calming with nationally available GIS data from NAVTEQ and to validate the traffic calming index with the percentage of children reported by school administrators as walking or biking to school, using data from a nationally representative sample of elementary schools in 2006-2010. Specific models, with and without correlated errors, examined associations of objective GIS measures of the built environment, nationally available from NAVTEQ, with the latent construct of traffic calming. The best fit model for the latent traffic calming construct was determined to be a five factor model including objective measures of intersection density, count of medians/dividers, count of low mobility streets, count of roundabouts, and count of on-street parking availability, with no correlated errors among items. This construct also proved to be a good fit for the full measurement model when the outcome measure of percentage of students walking or biking to school was added to the model. The traffic calming measure was strongly, significantly, and positively correlated with the percentage of students reported as walking or biking to school. Applicability of results to public health and transportation policies and practices are discussed.

On statistical inference in time series analysis of the evolution of road safety.

PubMed

Commandeur, Jacques J F; Bijleveld, Frits D; Bergel-Hayat, Ruth; Antoniou, Constantinos; Yannis, George; Papadimitriou, Eleonora

2013-11-01

Data collected for building a road safety observatory usually include observations made sequentially through time. Examples of such data, called time series data, include annual (or monthly) number of road traffic accidents, traffic fatalities or vehicle kilometers driven in a country, as well as the corresponding values of safety performance indicators (e.g., data on speeding, seat belt use, alcohol use, etc.). Some commonly used statistical techniques imply assumptions that are often violated by the special properties of time series data, namely serial dependency among disturbances associated with the observations. The first objective of this paper is to demonstrate the impact of such violations to the applicability of standard methods of statistical inference, which leads to an under or overestimation of the standard error and consequently may produce erroneous inferences. Moreover, having established the adverse consequences of ignoring serial dependency issues, the paper aims to describe rigorous statistical techniques used to overcome them. In particular, appropriate time series analysis techniques of varying complexity are employed to describe the development over time, relating the accident-occurrences to explanatory factors such as exposure measures or safety performance indicators, and forecasting the development into the near future. Traditional regression models (whether they are linear, generalized linear or nonlinear) are shown not to naturally capture the inherent dependencies in time series data. Dedicated time series analysis techniques, such as the ARMA-type and DRAG approaches are discussed next, followed by structural time series models, which are a subclass of state space methods. The paper concludes with general recommendations and practice guidelines for the use of time series models in road safety research. Copyright © 2012 Elsevier Ltd. All rights reserved.

Integration of Linear Dynamic Emission and Climate Models with Air Traffic Simulations

NASA Technical Reports Server (NTRS)

Sridhar, Banavar; Ng, Hok K.; Chen, Neil Y.

2012-01-01

Future air traffic management systems are required to balance the conflicting objectives of maximizing safety and efficiency of traffic flows while minimizing the climate impact of aviation emissions and contrails. Integrating emission and climate models together with air traffic simulations improve the understanding of the complex interaction between the physical climate system, carbon and other greenhouse gas emissions and aviation activity. This paper integrates a national-level air traffic simulation and optimization capability with simple climate models and carbon cycle models, and climate metrics to assess the impact of aviation on climate. The capability can be used to make trade-offs between extra fuel cost and reduction in global surface temperature change. The parameters in the simulation can be used to evaluate the effect of various uncertainties in emission models and contrails and the impact of different decision horizons. Alternatively, the optimization results from the simulation can be used as inputs to other tools that monetize global climate impacts like the FAA s Aviation Environmental Portfolio Management Tool for Impacts.

Traffic and related self-driven many-particle systems

NASA Astrophysics Data System (ADS)

Helbing, Dirk

2001-10-01

Since the subject of traffic dynamics has captured the interest of physicists, many surprising effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by ``phantom traffic jams'' even though drivers all like to drive fast? What are the mechanisms behind stop-and-go traffic? Why are there several different kinds of congestion, and how are they related? Why do most traffic jams occur considerably before the road capacity is reached? Can a temporary reduction in the volume of traffic cause a lasting traffic jam? Under which conditions can speed limits speed up traffic? Why do pedestrians moving in opposite directions normally organize into lanes, while similar systems ``freeze by heating''? All of these questions have been answered by applying and extending methods from statistical physics and nonlinear dynamics to self-driven many-particle systems. This article considers the empirical data and then reviews the main approaches to modeling pedestrian and vehicle traffic. These include microscopic (particle-based), mesoscopic (gas-kinetic), and macroscopic (fluid-dynamic) models. Attention is also paid to the formulation of a micro-macro link, to aspects of universality, and to other unifying concepts, such as a general modeling framework for self-driven many-particle systems, including spin systems. While the primary focus is upon vehicle and pedestrian traffic, applications to biological or socio-economic systems such as bacterial colonies, flocks of birds, panics, and stock market dynamics are touched upon as well.

Performance of color-dependent tasks of air traffic control specialists as a function of type and degree of color vision deficiency.

DOT National Transportation Integrated Search

1992-08-01

This experiment was conducted to expand initial efforts to validate the requirement for normal color vision in Air Traffic Control Specialist (ATCS) personnel who work at en route center, terminal, and flight service station facilities. An enlarged d...

Characterization, adaptive traffic shaping, and multiplexing of real-time MPEG II video

NASA Astrophysics Data System (ADS)

Agrawal, Sanjay; Barry, Charles F.; Binnai, Vinay; Kazovsky, Leonid G.

1997-01-01

We obtain network traffic model for real-time MPEG-II encoded digital video by analyzing video stream samples from real-time encoders from NUKO Information Systems. MPEG-II sample streams include a resolution intensive movie, City of Joy, an action intensive movie, Aliens, a luminance intensive (black and white) movie, Road To Utopia, and a chrominance intensive (color) movie, Dick Tracy. From our analysis we obtain a heuristic model for the encoded video traffic which uses a 15-stage Markov process to model the I,B,P frame sequences within a group of pictures (GOP). A jointly-correlated Gaussian process is used to model the individual frame sizes. Scene change arrivals are modeled according to a gamma process. Simulations show that our MPEG-II traffic model generates, I,B,P frame sequences and frame sizes that closely match the sample MPEG-II stream traffic characteristics as they relate to latency and buffer occupancy in network queues. To achieve high multiplexing efficiency we propose a traffic shaping scheme which sets preferred 1-frame generation times among a group of encoders so as to minimize the overall variation in total offered traffic while still allowing the individual encoders to react to scene changes. Simulations show that our scheme results in multiplexing gains of up to 10% enabling us to multiplex twenty 6 Mbps MPEG-II video streams instead of 18 streams over an ATM/SONET OC3 link without latency or cell loss penalty. This scheme is due for a patent.

A cellular automata model for traffic flow based on kinetics theory, vehicles capabilities and driver reactions

NASA Astrophysics Data System (ADS)

Guzmán, H. A.; Lárraga, M. E.; Alvarez-Icaza, L.; Carvajal, J.

2018-02-01

In this paper, a reliable cellular automata model oriented to faithfully reproduce deceleration and acceleration according to realistic reactions of drivers, when vehicles with different deceleration capabilities are considered is presented. The model focuses on describing complex traffic phenomena by coding in its rules the basic mechanisms of drivers behavior, vehicles capabilities and kinetics, while preserving simplicity. In particular, vehiclés kinetics is based on uniform accelerated motion, rather than in impulsive accelerated motion as in most existing CA models. Thus, the proposed model calculates in an analytic way three safe preserving distances to determine the best action a follower vehicle can take under a worst case scenario. Besides, the prediction analysis guarantees that under the proper assumptions, collision between vehicles may not happen at any future time. Simulations results indicate that all interactions of heterogeneous vehicles (i.e., car-truck, truck-car, car-car and truck-truck) are properly reproduced by the model. In addition, the model overcomes one of the major limitations of CA models for traffic modeling: the inability to perform smooth approach to slower or stopped vehicles. Moreover, the model is also capable of reproducing most empirical findings including the backward speed of the downstream front of the traffic jam, and different congested traffic patterns induced by a system with open boundary conditions with an on-ramp. Like most CA models, integer values are used to make the model run faster, which makes the proposed model suitable for real time traffic simulation of large networks.

Simulation of three lanes one-way freeway in low visibility weather by possible traffic accidents

NASA Astrophysics Data System (ADS)

Pang, Ming-bao; Zheng, Sha-sha; Cai, Zhang-hui

2015-09-01

The aim of this work is to investigate the traffic impact of low visibility weather on a freeway including the fraction of real vehicle rear-end accidents and road traffic capacity. Based on symmetric two-lane Nagel-Schreckenberg (STNS) model, a cellular automaton model of three-lane freeway mainline with the real occurrence of rear-end accidents in low visibility weather, which considers delayed reaction time and deceleration restriction, was established with access to real-time traffic information of intelligent transportation system (ITS). The characteristics of traffic flow in different visibility weather were discussed via the simulation experiments. The results indicate that incoming flow control (decreasing upstream traffic volume) and inputting variable speed limits (VSL) signal are effective in accident reducing and road actual traffic volume's enhancing. According to different visibility and traffic demand the appropriate control strategies should be adopted in order to not only decrease the probability of vehicle accidents but also avoid congestion.

The Fusion Model of Intelligent Transportation Systems Based on the Urban Traffic Ontology

NASA Astrophysics Data System (ADS)

Yang, Wang-Dong; Wang, Tao

On these issues unified representation of urban transport information using urban transport ontology, it defines the statute and the algebraic operations of semantic fusion in ontology level in order to achieve the fusion of urban traffic information in the semantic completeness and consistency. Thus this paper takes advantage of the semantic completeness of the ontology to build urban traffic ontology model with which we resolve the problems as ontology mergence and equivalence verification in semantic fusion of traffic information integration. Information integration in urban transport can increase the function of semantic fusion, and reduce the amount of data integration of urban traffic information as well enhance the efficiency and integrity of traffic information query for the help, through the practical application of intelligent traffic information integration platform of Changde city, the paper has practically proved that the semantic fusion based on ontology increases the effect and efficiency of the urban traffic information integration, reduces the storage quantity, and improve query efficiency and information completeness.

A Study on Urban Road Traffic Safety Based on Matter Element Analysis

PubMed Central

Hu, Qizhou; Zhou, Zhuping; Sun, Xu

2014-01-01

This paper examines a new evaluation of urban road traffic safety based on a matter element analysis, avoiding the difficulties found in other traffic safety evaluations. The issue of urban road traffic safety has been investigated through the matter element analysis theory. The chief aim of the present work is to investigate the features of urban road traffic safety. Emphasis was placed on the construction of a criterion function by which traffic safety achieved a hierarchical system of objectives to be evaluated. The matter element analysis theory was used to create the comprehensive appraisal model of urban road traffic safety. The technique was used to employ a newly developed and versatile matter element analysis algorithm. The matter element matrix solves the uncertainty and incompatibility of the evaluated factors used to assess urban road traffic safety. The application results showed the superiority of the evaluation model and a didactic example was included to illustrate the computational procedure. PMID:25587267

Mitochondrial metabolism in Parkinson's disease impairs quality control autophagy by hampering microtubule-dependent traffic

PubMed Central

Arduíno, Daniela M.; Raquel Esteves, A.; Cortes, Luísa; Silva, Diana F.; Patel, Bindi; Grazina, Manuela; Swerdlow, Russell H.; Oliveira, Catarina R.; Cardoso, Sandra M.

2012-01-01

Abnormal presence of autophagic vacuoles is evident in brains of patients with Parkinson's disease (PD), in contrast to the rare detection of autophagosomes in a normal brain. However, the actual cause and pathological significance of these observations remain unknown. Here, we demonstrate a role for mitochondrial metabolism in the regulation of the autophagy-lysosomal pathway in ex vivo and in vitro models of PD. We show that transferring mitochondria from PD patients into cells previously depleted of mitochondrial DNA is sufficient to reproduce the alterations in the autophagic system observed in PD patient brains. Although the initial steps of this pathway are not compromised, there is an increased accumulation of autophagosomes associated with a defective autophagic activity. We prove that this functional decline was originated from a deficient mobilization of autophagosomes from their site of formation toward lysosomes due to disruption in microtubule-dependent trafficking. This contributed directly to a decreased proteolytic flux of α-synuclein and other autophagic substrates. Our results lend strong support for a direct impact of mitochondria in autophagy as defective autophagic clearance ability secondary to impaired microtubule trafficking is driven by dysfunctional mitochondria. We uncover mitochondria and mitochondria-dependent intracellular traffic as main players in the regulation of autophagy in PD. PMID:22843496

Mitochondrial metabolism in Parkinson's disease impairs quality control autophagy by hampering microtubule-dependent traffic.

PubMed

Arduíno, Daniela M; Esteves, A Raquel; Cortes, Luísa; Silva, Diana F; Patel, Bindi; Grazina, Manuela; Swerdlow, Russell H; Oliveira, Catarina R; Cardoso, Sandra M

2012-11-01

Abnormal presence of autophagic vacuoles is evident in brains of patients with Parkinson's disease (PD), in contrast to the rare detection of autophagosomes in a normal brain. However, the actual cause and pathological significance of these observations remain unknown. Here, we demonstrate a role for mitochondrial metabolism in the regulation of the autophagy-lysosomal pathway in ex vivo and in vitro models of PD. We show that transferring mitochondria from PD patients into cells previously depleted of mitochondrial DNA is sufficient to reproduce the alterations in the autophagic system observed in PD patient brains. Although the initial steps of this pathway are not compromised, there is an increased accumulation of autophagosomes associated with a defective autophagic activity. We prove that this functional decline was originated from a deficient mobilization of autophagosomes from their site of formation toward lysosomes due to disruption in microtubule-dependent trafficking. This contributed directly to a decreased proteolytic flux of α-synuclein and other autophagic substrates. Our results lend strong support for a direct impact of mitochondria in autophagy as defective autophagic clearance ability secondary to impaired microtubule trafficking is driven by dysfunctional mitochondria. We uncover mitochondria and mitochondria-dependent intracellular traffic as main players in the regulation of autophagy in PD.

Comparison of classical statistical methods and artificial neural network in traffic noise prediction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nedic, Vladimir, E-mail: vnedic@kg.ac.rs; Despotovic, Danijela, E-mail: ddespotovic@kg.ac.rs; Cvetanovic, Slobodan, E-mail: slobodan.cvetanovic@eknfak.ni.ac.rs

2014-11-15

Traffic is the main source of noise in urban environments and significantly affects human mental and physical health and labor productivity. Therefore it is very important to model the noise produced by various vehicles. Techniques for traffic noise prediction are mainly based on regression analysis, which generally is not good enough to describe the trends of noise. In this paper the application of artificial neural networks (ANNs) for the prediction of traffic noise is presented. As input variables of the neural network, the proposed structure of the traffic flow and the average speed of the traffic flow are chosen. Themore » output variable of the network is the equivalent noise level in the given time period L{sub eq}. Based on these parameters, the network is modeled, trained and tested through a comparative analysis of the calculated values and measured levels of traffic noise using the originally developed user friendly software package. It is shown that the artificial neural networks can be a useful tool for the prediction of noise with sufficient accuracy. In addition, the measured values were also used to calculate equivalent noise level by means of classical methods, and comparative analysis is given. The results clearly show that ANN approach is superior in traffic noise level prediction to any other statistical method. - Highlights: • We proposed an ANN model for prediction of traffic noise. • We developed originally designed user friendly software package. • The results are compared with classical statistical methods. • The results are much better predictive capabilities of ANN model.« less

Understanding how roadside concentrations of NOx are influenced by the background levels, traffic density, and meteorological conditions using Boosted Regression Trees

NASA Astrophysics Data System (ADS)

Sayegh, Arwa; Tate, James E.; Ropkins, Karl

2016-02-01

Oxides of Nitrogen (NOx) is a major component of photochemical smog and its constituents are considered principal traffic-related pollutants affecting human health. This study investigates the influence of background concentrations of NOx, traffic density, and prevailing meteorological conditions on roadside concentrations of NOx at UK urban, open motorway, and motorway tunnel sites using the statistical approach Boosted Regression Trees (BRT). BRT models have been fitted using hourly concentration, traffic, and meteorological data for each site. The models predict, rank, and visualise the relationship between model variables and roadside NOx concentrations. A strong relationship between roadside NOx and monitored local background concentrations is demonstrated. Relationships between roadside NOx and other model variables have been shown to be strongly influenced by the quality and resolution of background concentrations of NOx, i.e. if it were based on monitored data or modelled prediction. The paper proposes a direct method of using site-specific fundamental diagrams for splitting traffic data into four traffic states: free-flow, busy-flow, congested, and severely congested. Using BRT models, the density of traffic (vehicles per kilometre) was observed to have a proportional influence on the concentrations of roadside NOx, with different fitted regression line slopes for the different traffic states. When other influences are conditioned out, the relationship between roadside concentrations and ambient air temperature suggests NOx concentrations reach a minimum at around 22 °C with high concentrations at low ambient air temperatures which could be associated to restricted atmospheric dispersion and/or to changes in road traffic exhaust emission characteristics at low ambient air temperatures. This paper uses BRT models to study how different critical factors, and their relative importance, influence the variation of roadside NOx concentrations. The paper highlights the importance of either setting up local background continuous monitors or improving the quality and resolution of modelled UK background maps and the need to further investigate the influence of ambient air temperature on NOx emissions and roadside NOx concentrations.

An extended lattice model accounting for traffic jerk

NASA Astrophysics Data System (ADS)

Redhu, Poonam; Siwach, Vikash

2018-02-01

In this paper, a flux difference lattice hydrodynamics model is extended by considering the traffic jerk effect which comes due to vehicular motion of non-motor automobiles. The effect of traffic jerk has been examined through linear stability analysis and shown that it can significantly enlarge the unstable region on the phase diagram. To describe the phase transition of traffic flow, mKdV equation near the critical point is derived through nonlinear stability analysis. The theoretical findings have been verified using numerical simulation which confirms that the jerk parameter plays an important role in stabilizing the traffic jam efficiently in sensing the flux difference of leading sites.

Detection of Road Markings Recorded in In-Vehicle Camera Images by Using Position-Dependent Classifiers

NASA Astrophysics Data System (ADS)

Noda, Masafumi; Takahashi, Tomokazu; Deguchi, Daisuke; Ide, Ichiro; Murase, Hiroshi; Kojima, Yoshiko; Naito, Takashi

In this study, we propose a method for detecting road markings recorded in an image captured by an in-vehicle camera by using a position-dependent classifier. Road markings are symbols painted on the road surface that help in preventing traffic accidents and in ensuring traffic smooth. Therefore, driver support systems for detecting road markings, such as a system that provides warning in the case when traffic signs are overlooked, and supporting the stopping of a vehicle are required. It is difficult to detect road markings because their appearance changes with the actual traffic conditions, e. g. the shape and resolution change. The variation in these appearances depend on the positional relation between the vehicle and the road markings, and on the vehicle posture. Although these variations are quite large in an entire image, they are relatively small in a local area of the image. Therefore, we try to improve the detection performance by taking into account the local variations in these appearances. We propose a method in which a position-dependent classifier is used to detect road markings recorded in images captured by an in-vehicle camera. Further, to train the classifier efficiently, we propose a generative learning method that takes into consideration the positional relation between the vehicle and road markings, and also the vehicle posture. Experimental results showed that the detection performance when the proposed method was used was better than when a method involving a single classifier was used.

Retromer guides STxB and CD8-M6PR from early to recycling endosomes, EHD1 guides STxB from recycling endosome to Golgi

PubMed Central

McKenzie, Jenna E.; Raisley, Brent; Zhou, Xin; Naslavsky, Naava; Taguchi, Tomohiko; Caplan, Steve; Sheff, David

2012-01-01

Retrograde trafficking transports proteins, lipids and toxins from the plasma membrane to the Golgi and ER. To reach the Golgi, these cargos must transit the endosomal system, consisting of early endosomes, recycling endosomes, late endosomes and lysosomes. All cargos pass through early endosomes, but may take different routes to the Golgi. Retromer dependent cargos bypass the late endosomes to reach the Golgi. We compared how two very different retromer dependent cargos negotiate the endosomal sorting system. Shiga toxin B, bound to the external layer of the plasma membrane, and chimeric CD8-Mannose-6-Phosphate Receptor, which is anchored via a transmembrane domain. Both appear to pass through the recycling endosome. Ablation of the recycling endosome diverted both of these cargos to an aberrant compartment and prevented them from reaching the Golgi. Once in the recycling endosome, Shiga toxin required EHD1 to traffic to the TGN, while the CD8-Mannose-6-Phosphate Receptor was not significantly dependent on EHD1. Knockdown of retromer components left cargo in the early endosomes, suggesting that it is required for retrograde exit from this compartment. This work establishes the recycling endosome as a required step in retrograde traffic of at least these two retromer dependent cargos. Along this pathway, retromer is associated with EE to recycling endosome traffic, while EHD1 is associated with recycling endosome to TGN traffic of STxB. PMID:22540229

Fine-Tuning ADAS Algorithm Parameters for Optimizing Traffic ...

EPA Pesticide Factsheets

With the development of the Connected Vehicle technology that facilitates wirelessly communication among vehicles and road-side infrastructure, the Advanced Driver Assistance Systems (ADAS) can be adopted as an effective tool for accelerating traffic safety and mobility optimization at various highway facilities. To this end, the traffic management centers identify the optimal ADAS algorithm parameter set that enables the maximum improvement of the traffic safety and mobility performance, and broadcast the optimal parameter set wirelessly to individual ADAS-equipped vehicles. After adopting the optimal parameter set, the ADAS-equipped drivers become active agents in the traffic stream that work collectively and consistently to prevent traffic conflicts, lower the intensity of traffic disturbances, and suppress the development of traffic oscillations into heavy traffic jams. Successful implementation of this objective requires the analysis capability of capturing the impact of the ADAS on driving behaviors, and measuring traffic safety and mobility performance under the influence of the ADAS. To address this challenge, this research proposes a synthetic methodology that incorporates the ADAS-affected driving behavior modeling and state-of-the-art microscopic traffic flow modeling into a virtually simulated environment. Building on such an environment, the optimal ADAS algorithm parameter set is identified through an optimization programming framework to enable th

Using temporal detrending to observe the spatial correlation of traffic.

PubMed

Ermagun, Alireza; Chatterjee, Snigdhansu; Levinson, David

2017-01-01

This empirical study sheds light on the spatial correlation of traffic links under different traffic regimes. We mimic the behavior of real traffic by pinpointing the spatial correlation between 140 freeway traffic links in a major sub-network of the Minneapolis-St. Paul freeway system with a grid-like network topology. This topology enables us to juxtapose the positive and negative correlation between links, which has been overlooked in short-term traffic forecasting models. To accurately and reliably measure the correlation between traffic links, we develop an algorithm that eliminates temporal trends in three dimensions: (1) hourly dimension, (2) weekly dimension, and (3) system dimension for each link. The spatial correlation of traffic links exhibits a stronger negative correlation in rush hours, when congestion affects route choice. Although this correlation occurs mostly in parallel links, it is also observed upstream, where travelers receive information and are able to switch to substitute paths. Irrespective of the time-of-day and day-of-week, a strong positive correlation is witnessed between upstream and downstream links. This correlation is stronger in uncongested regimes, as traffic flow passes through consecutive links more quickly and there is no congestion effect to shift or stall traffic. The extracted spatial correlation structure can augment the accuracy of short-term traffic forecasting models.

Using temporal detrending to observe the spatial correlation of traffic

PubMed Central

2017-01-01

This empirical study sheds light on the spatial correlation of traffic links under different traffic regimes. We mimic the behavior of real traffic by pinpointing the spatial correlation between 140 freeway traffic links in a major sub-network of the Minneapolis—St. Paul freeway system with a grid-like network topology. This topology enables us to juxtapose the positive and negative correlation between links, which has been overlooked in short-term traffic forecasting models. To accurately and reliably measure the correlation between traffic links, we develop an algorithm that eliminates temporal trends in three dimensions: (1) hourly dimension, (2) weekly dimension, and (3) system dimension for each link. The spatial correlation of traffic links exhibits a stronger negative correlation in rush hours, when congestion affects route choice. Although this correlation occurs mostly in parallel links, it is also observed upstream, where travelers receive information and are able to switch to substitute paths. Irrespective of the time-of-day and day-of-week, a strong positive correlation is witnessed between upstream and downstream links. This correlation is stronger in uncongested regimes, as traffic flow passes through consecutive links more quickly and there is no congestion effect to shift or stall traffic. The extracted spatial correlation structure can augment the accuracy of short-term traffic forecasting models. PMID:28472093

Analysis and comparison of safety models using average daily, average hourly, and microscopic traffic.

PubMed

Wang, Ling; Abdel-Aty, Mohamed; Wang, Xuesong; Yu, Rongjie

2018-02-01

There have been plenty of traffic safety studies based on average daily traffic (ADT), average hourly traffic (AHT), or microscopic traffic at 5 min intervals. Nevertheless, not enough research has compared the performance of these three types of safety studies, and seldom of previous studies have intended to find whether the results of one type of study is transferable to the other two studies. First, this study built three models: a Bayesian Poisson-lognormal model to estimate the daily crash frequency using ADT, a Bayesian Poisson-lognormal model to estimate the hourly crash frequency using AHT, and a Bayesian logistic regression model for the real-time safety analysis using microscopic traffic. The model results showed that the crash contributing factors found by different models were comparable but not the same. Four variables, i.e., the logarithm of volume, the standard deviation of speed, the logarithm of segment length, and the existence of diverge segment, were positively significant in the three models. Additionally, weaving segments experienced higher daily and hourly crash frequencies than merge and basic segments. Then, each of the ADT-based, AHT-based, and real-time models was used to estimate safety conditions at different levels: daily and hourly, meanwhile, the real-time model was also used in 5 min intervals. The results uncovered that the ADT- and AHT-based safety models performed similar in predicting daily and hourly crash frequencies, and the real-time safety model was able to provide hourly crash frequency. Copyright © 2017 Elsevier Ltd. All rights reserved.

Memory effects in microscopic traffic models and wide scattering in flow-density data

NASA Astrophysics Data System (ADS)

Treiber, Martin; Helbing, Dirk

2003-10-01

By means of microscopic simulations we show that noninstantaneous adaptation of the driving behavior to the traffic situation together with the conventional method to measure flow-density data provides a possible explanation for the observed inverse-λ shape and the wide scattering of flow-density data in “synchronized” congested traffic. We model a memory effect in the response of drivers to the traffic situation for a wide class of car-following models by introducing an additional dynamical variable (the “subjective level of service”) describing the adaptation of drivers to the surrounding traffic situation during the past few minutes and couple this internal state to parameters of the underlying model that are related to the driving style. For illustration, we use the intelligent-driver model (IDM) as the underlying model, characterize the level of service solely by the velocity, and couple the internal variable to the IDM parameter “time gap” to model an increase of the time gap in congested traffic (“frustration effect”), which is supported by single-vehicle data. We simulate open systems with a bottleneck and obtain flow-density data by implementing “virtual detectors.” The shape, relative size, and apparent “stochasticity” of the region of the scattered data points agree nearly quantitatively with empirical data. Wide scattering is even observed for identical vehicles, although the proposed model is a time-continuous, deterministic, single-lane car-following model with a unique fundamental diagram.

Modeling and analyses for an extended car-following model accounting for drivers' situation awareness from cyber physical perspective

NASA Astrophysics Data System (ADS)

Chen, Dong; Sun, Dihua; Zhao, Min; Zhou, Tong; Cheng, Senlin

2018-07-01

In fact, driving process is a typical cyber physical process which couples tightly the cyber factor of traffic information with the physical components of the vehicles. Meanwhile, the drivers have situation awareness in driving process, which is not only ascribed to the current traffic states, but also extrapolates the changing trend. In this paper, an extended car-following model is proposed to account for drivers' situation awareness. The stability criterion of the proposed model is derived via linear stability analysis. The results show that the stable region of proposed model will be enlarged on the phase diagram compared with previous models. By employing the reductive perturbation method, the modified Korteweg de Vries (mKdV) equation is obtained. The kink-antikink soliton of mKdV equation reveals theoretically the evolution of traffic jams. Numerical simulations are conducted to verify the analytical results. Two typical traffic Scenarios are investigated. The simulation results demonstrate that drivers' situation awareness plays a key role in traffic flow oscillations and the congestion transition.

An integrated approach to evaluate policies for controlling traffic law violations.

PubMed

Mehmood, Arif

2010-03-01

Modeling dynamics of the driver behavior is a complex problem. In this paper a system approach is introduced to model and to analyze the driver behavior related to traffic law violations in the Emirate of Abu Dhabi. This paper demonstrates how the theoretical relationships between different factors can be expressed formally, and how the resulting model can assist in evaluating potential benefits of various policies to control the traffic law violations Using system approach, an integrated dynamic simulation model is developed, and model is tested to simulate the driver behavior for violating traffic laws during 2002-2007 in the Emirate of Abu Dhabi. The dynamic simulation model attempts to address the questions: (1) "what" interventions should be implemented to reduce and eventually control traffic violations which will lead to improving road safety and (2) "how" to justify those interventions will be effective or ineffective to control the violations in different transportation conditions. The simulation results reveal promising capability of applying system approach in the policy evaluation studies. Copyright 2009 Elsevier Ltd. All rights reserved.

Safety analysis of urban signalized intersections under mixed traffic.

PubMed

S, Anjana; M V L R, Anjaneyulu

2015-02-01

This study examined the crash causative factors of signalized intersections under mixed traffic using advanced statistical models. Hierarchical Poisson regression and logistic regression models were developed to predict the crash frequency and severity of signalized intersection approaches. The prediction models helped to develop general safety countermeasures for signalized intersections. The study shows that exclusive left turn lanes and countdown timers are beneficial for improving the safety of signalized intersections. Safety is also influenced by the presence of a surveillance camera, green time, median width, traffic volume, and proportion of two wheelers in the traffic stream. The factors that influence the severity of crashes were also identified in this study. As a practical application, the safe values of deviation of green time provided from design green time, with varying traffic volume, is presented in this study. This is a useful tool for setting the appropriate green time for a signalized intersection approach with variations in the traffic volume. Copyright © 2014 Elsevier Ltd. All rights reserved.

Predicting commuter flows in spatial networks using a radiation model based on temporal ranges

NASA Astrophysics Data System (ADS)

Ren, Yihui; Ercsey-Ravasz, Mária; Wang, Pu; González, Marta C.; Toroczkai, Zoltán

2014-11-01

Understanding network flows such as commuter traffic in large transportation networks is an ongoing challenge due to the complex nature of the transportation infrastructure and human mobility. Here we show a first-principles based method for traffic prediction using a cost-based generalization of the radiation model for human mobility, coupled with a cost-minimizing algorithm for efficient distribution of the mobility fluxes through the network. Using US census and highway traffic data, we show that traffic can efficiently and accurately be computed from a range-limited, network betweenness type calculation. The model based on travel time costs captures the log-normal distribution of the traffic and attains a high Pearson correlation coefficient (0.75) when compared with real traffic. Because of its principled nature, this method can inform many applications related to human mobility driven flows in spatial networks, ranging from transportation, through urban planning to mitigation of the effects of catastrophic events.

General phase transition models for vehicular traffic with point constraints on the flow

NASA Astrophysics Data System (ADS)

Dal Santo, E.; Rosini, M. D.; Dymski, N.; Benyahia, M.

2017-12-01

We generalize the phase transition model studied in [R. Colombo. Hyperbolic phase transition in traffic flow.\\ SIAM J.\\ Appl.\\ Math., 63(2):708-721, 2002], that describes the evolution of vehicular traffic along a one-lane road. Two different phases are taken into account, according to whether the traffic is low or heavy. The model is given by a scalar conservation law in the \\emph{free-flow} phase and by a system of two conservation laws in the \\emph{congested} phase. In particular, we study the resulting Riemann problems in the case a local point constraint on the flux of the solutions is enforced.

Chaotic Ising-like dynamics in traffic signals

PubMed Central

Suzuki, Hideyuki; Imura, Jun-ichi; Aihara, Kazuyuki

2013-01-01

The green and red lights of a traffic signal can be viewed as the up and down states of an Ising spin. Moreover, traffic signals in a city interact with each other, if they are controlled in a decentralised way. In this paper, a simple model of such interacting signals on a finite-size two-dimensional lattice is shown to have Ising-like dynamics that undergoes a ferromagnetic phase transition. Probabilistic behaviour of the model is realised by chaotic billiard dynamics that arises from coupled non-chaotic elements. This purely deterministic model is expected to serve as a starting point for considering statistical mechanics of traffic signals. PMID:23350034

Developing a stochastic traffic volume prediction model for public-private partnership projects

NASA Astrophysics Data System (ADS)

Phong, Nguyen Thanh; Likhitruangsilp, Veerasak; Onishi, Masamitsu

2017-11-01

Transportation projects require an enormous amount of capital investment resulting from their tremendous size, complexity, and risk. Due to the limitation of public finances, the private sector is invited to participate in transportation project development. The private sector can entirely or partially invest in transportation projects in the form of Public-Private Partnership (PPP) scheme, which has been an attractive option for several developing countries, including Vietnam. There are many factors affecting the success of PPP projects. The accurate prediction of traffic volume is considered one of the key success factors of PPP transportation projects. However, only few research works investigated how to predict traffic volume over a long period of time. Moreover, conventional traffic volume forecasting methods are usually based on deterministic models which predict a single value of traffic volume but do not consider risk and uncertainty. This knowledge gap makes it difficult for concessionaires to estimate PPP transportation project revenues accurately. The objective of this paper is to develop a probabilistic traffic volume prediction model. First, traffic volumes were estimated following the Geometric Brownian Motion (GBM) process. Monte Carlo technique is then applied to simulate different scenarios. The results show that this stochastic approach can systematically analyze variations in the traffic volume and yield more reliable estimates for PPP projects.

A Novel Biobjective Risk-Based Model for Stochastic Air Traffic Network Flow Optimization Problem.

PubMed

Cai, Kaiquan; Jia, Yaoguang; Zhu, Yanbo; Xiao, Mingming

2015-01-01

Network-wide air traffic flow management (ATFM) is an effective way to alleviate demand-capacity imbalances globally and thereafter reduce airspace congestion and flight delays. The conventional ATFM models assume the capacities of airports or airspace sectors are all predetermined. However, the capacity uncertainties due to the dynamics of convective weather may make the deterministic ATFM measures impractical. This paper investigates the stochastic air traffic network flow optimization (SATNFO) problem, which is formulated as a weighted biobjective 0-1 integer programming model. In order to evaluate the effect of capacity uncertainties on ATFM, the operational risk is modeled via probabilistic risk assessment and introduced as an extra objective in SATNFO problem. Computation experiments using real-world air traffic network data associated with simulated weather data show that presented model has far less constraints compared to stochastic model with nonanticipative constraints, which means our proposed model reduces the computation complexity.

Modeling self-consistent multi-class dynamic traffic flow

NASA Astrophysics Data System (ADS)

Cho, Hsun-Jung; Lo, Shih-Ching

2002-09-01

In this study, we present a systematic self-consistent multiclass multilane traffic model derived from the vehicular Boltzmann equation and the traffic dispersion model. The multilane domain is considered as a two-dimensional space and the interaction among vehicles in the domain is described by a dispersion model. The reason we consider a multilane domain as a two-dimensional space is that the driving behavior of road users may not be restricted by lanes, especially motorcyclists. The dispersion model, which is a nonlinear Poisson equation, is derived from the car-following theory and the equilibrium assumption. Under the concept that all kinds of users share the finite section, the density is distributed on a road by the dispersion model. In addition, the dynamic evolution of the traffic flow is determined by the systematic gas-kinetic model derived from the Boltzmann equation. Multiplying Boltzmann equation by the zeroth, first- and second-order moment functions, integrating both side of the equation and using chain rules, we can derive continuity, motion and variance equation, respectively. However, the second-order moment function, which is the square of the individual velocity, is employed by previous researches does not have physical meaning in traffic flow. Although the second-order expansion results in the velocity variance equation, additional terms may be generated. The velocity variance equation we propose is derived from multiplying Boltzmann equation by the individual velocity variance. It modifies the previous model and presents a new gas-kinetic traffic flow model. By coupling the gas-kinetic model and the dispersion model, a self-consistent system is presented.

Performance Analyses and Improvements for the IEEE 802.15.4 CSMA/CA Scheme with Heterogeneous Buffered Conditions

PubMed Central

Zhu, Jianping; Tao, Zhengsu; Lv, Chunfeng

2012-01-01

Studies of the IEEE 802.15.4 Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) scheme have been received considerable attention recently, with most of these studies focusing on homogeneous or saturated traffic. Two novel transmission schemes—OSTS/BSTS (One Service a Time Scheme/Bulk Service a Time Scheme)—are proposed in this paper to improve the behaviors of time-critical buffered networks with heterogeneous unsaturated traffic. First, we propose a model which contains two modified semi-Markov chains and a macro-Markov chain combined with the theory of M/G/1/K queues to evaluate the characteristics of these two improved CSMA/CA schemes, in which traffic arrivals and accessing packets are bestowed with non-preemptive priority over each other, instead of prioritization. Then, throughput, packet delay and energy consumption of unsaturated, unacknowledged IEEE 802.15.4 beacon-enabled networks are predicted based on the overall point of view which takes the dependent interactions of different types of nodes into account. Moreover, performance comparisons of these two schemes with other non-priority schemes are also proposed. Analysis and simulation results show that delay and fairness of our schemes are superior to those of other schemes, while throughput and energy efficiency are superior to others in more heterogeneous situations. Comprehensive simulations demonstrate that the analysis results of these models match well with the simulation results. PMID:22666076

Performance analyses and improvements for the IEEE 802.15.4 CSMA/CA scheme with heterogeneous buffered conditions.

PubMed

Zhu, Jianping; Tao, Zhengsu; Lv, Chunfeng

2012-01-01

Studies of the IEEE 802.15.4 Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) scheme have been received considerable attention recently, with most of these studies focusing on homogeneous or saturated traffic. Two novel transmission schemes-OSTS/BSTS (One Service a Time Scheme/Bulk Service a Time Scheme)-are proposed in this paper to improve the behaviors of time-critical buffered networks with heterogeneous unsaturated traffic. First, we propose a model which contains two modified semi-Markov chains and a macro-Markov chain combined with the theory of M/G/1/K queues to evaluate the characteristics of these two improved CSMA/CA schemes, in which traffic arrivals and accessing packets are bestowed with non-preemptive priority over each other, instead of prioritization. Then, throughput, packet delay and energy consumption of unsaturated, unacknowledged IEEE 802.15.4 beacon-enabled networks are predicted based on the overall point of view which takes the dependent interactions of different types of nodes into account. Moreover, performance comparisons of these two schemes with other non-priority schemes are also proposed. Analysis and simulation results show that delay and fairness of our schemes are superior to those of other schemes, while throughput and energy efficiency are superior to others in more heterogeneous situations. Comprehensive simulations demonstrate that the analysis results of these models match well with the simulation results.

Streamlining Transportation Corridor Planning Processess: Freight and Traffic Information

DOE Office of Scientific and Technical Information (OSTI.GOV)

Franzese, Oscar

2010-08-01

The traffic investigation is one of the most important parts of an Environmental Impact Statement of projects involving the construction of new roadway facilities and/or the improvement of existing ones. The focus of the traffic analysis is on the determination of anticipated traffic flow characteristics of the proposed project, by the application of analytical methods that can be grouped under the umbrella of capacity analysis methodologies. In general, the main traffic parameter used in EISs to describe the quality of traffic flow is the Level of Service (LOS). The current state of the practice in terms of the traffic investigationsmore » for EISs has two main shortcomings. The first one is related to the information that is necessary to conduct the traffic analysis, and specifically to the lack of integration among the different transportation models and the sources of information that, in general, reside in GIS databases. A discussion of the benefits of integrating CRS&SI technologies and the transportation models used in the EIS traffic investigation is included. The second shortcoming is in the presentation of the results, both in terms of the appearance and formatting, as well as content. The presentation of traffic results (current and proposed) is discussed. This chapter also addresses the need of additional data, in terms of content and coverage. Regarding the former, other traffic parameters (e.g., delays) that are more meaningful to non-transportation experts than LOS, as well as additional information (e.g., freight flows) that can impact traffic conditions and safety are discussed. Spatial information technologies can decrease the negative effects of, and even eliminate, these shortcomings by making the relevant information that is input to the models more complete and readily available, and by providing the means to communicate the results in a more clear and efficient manner. The benefits that the application and use of CRS&SI technologies can provide to improve and expedite the traffic investigation part of the EIS process are presented.« less

A retrospective evaluation of traffic forecasting techniques.

DOT National Transportation Integrated Search

2016-08-01

Traffic forecasting techniquessuch as extrapolation of previous years traffic volumes, regional travel demand models, or : local trip generation rateshelp planners determine needed transportation improvements. Thus, knowing the accuracy of t...

Nonlinear stability of traffic models and the use of Lyapunov vectors for estimating the traffic state

NASA Astrophysics Data System (ADS)

Palatella, Luigi; Trevisan, Anna; Rambaldi, Sandro

2013-08-01

Valuable information for estimating the traffic flow is obtained with current GPS technology by monitoring position and velocity of vehicles. In this paper, we present a proof of concept study that shows how the traffic state can be estimated using only partial and noisy data by assimilating them in a dynamical model. Our approach is based on a data assimilation algorithm, developed by the authors for chaotic geophysical models, designed to be equivalent but computationally much less demanding than the traditional extended Kalman filter. Here we show that the algorithm is even more efficient if the system is not chaotic and demonstrate by numerical experiments that an accurate reconstruction of the complete traffic state can be obtained at a very low computational cost by monitoring only a small percentage of vehicles.

Evaluation of air traffic control models and simulations.

DOT National Transportation Integrated Search

1971-06-01

Approximately two hundred reports were identified as describing Air Traffic Control (ATC) modeling and simulation efforts. Of these, about ninety analytical and simulation models dealing with virtually all aspects of ATC were formally evaluated. The ...

The Aviation System Analysis Capability Airport Capacity and Delay Models

NASA Technical Reports Server (NTRS)

Lee, David A.; Nelson, Caroline; Shapiro, Gerald

1998-01-01

The ASAC Airport Capacity Model and the ASAC Airport Delay Model support analyses of technologies addressing airport capacity. NASA's Aviation System Analysis Capability (ASAC) Airport Capacity Model estimates the capacity of an airport as a function of weather, Federal Aviation Administration (FAA) procedures, traffic characteristics, and the level of technology available. Airport capacity is presented as a Pareto frontier of arrivals per hour versus departures per hour. The ASAC Airport Delay Model allows the user to estimate the minutes of arrival delay for an airport, given its (weather dependent) capacity. Historical weather observations and demand patterns are provided by ASAC as inputs to the delay model. The ASAC economic models can translate a reduction in delay minutes into benefit dollars.

A computerized traffic control algorithm to determine optimal traffic signal settings. Ph.D. Thesis - Toledo Univ.

NASA Technical Reports Server (NTRS)

Seldner, K.

1977-01-01

An algorithm was developed to optimally control the traffic signals at each intersection using a discrete time traffic model applicable to heavy or peak traffic. Off line optimization procedures were applied to compute the cycle splits required to minimize the lengths of the vehicle queues and delay at each intersection. The method was applied to an extensive traffic network in Toledo, Ohio. Results obtained with the derived optimal settings are compared with the control settings presently in use.

The Impact of the Thai Motorcycle Transition on Road Traffic Injury: Thai Cohort Study Results

PubMed Central

Berecki-Gisolf, Janneke; Yiengprugsawan, Vasoontara; Kelly, Matthew; McClure, Roderick; Seubsman, Sam-ang; Sleigh, Adrian

2015-01-01

Objectives The aim of this study was to investigate the impact of motorcycle to car transitioning and urbanisation on traffic injury rates in Thailand. Design Analysis of two consecutive surveys of a large national cohort study. Setting Thailand. Participants The data derived from 57,154 Thai Cohort Study (TCS) participants who provided relevant data on both the 2005 and 2009 surveys. Primary and secondary outcome measures Motorcycle and car traffic crash injury self-reported in 2009, with twelve months’ recall. Results In 2009, 5608(10%) participants reported a traffic crash injury. Most crashes involved a motorcycle (74%). Car access increased and motorcycle use decreased between 2005 and 2009. Among those who used a motorcycle at both time points, traffic injury incidence was 2.8 times greater compared to those who did not use a motorcycle at either time point. Multivariable logistic regression models were used to test longitudinal and cross sectional factors associated with traffic crash injury: in the adjusted model, cars were negatively and motorcycles positively associated with injury. Living in an urban area was not injury protective in the adjusted model of traffic crash injury. Conclusions Ongoing urbanisation in Thailand can be expected to lead to further reductions in road traffic injuries based on transition from motorcycles to cars in urban areas. Cities, however, do not provide an intrinsically safer traffic environment. To accommodate a safe transition to car use in Thailand, traffic infrastructural changes anticipating the growing car density in urban areas is warranted. PMID:25826214

Epidemiologic Pattern of Fatal Traffic Injuries among Iranian Drivers; 2004–2010

PubMed Central

BAKHTIYARI, Mahmood; MEHMANDAR, Mohammad Reza; RIAHI, Seyed Mohammad; MANSOURNIA, Mohammad Ali; SARTIPI, Majid; BAHADORIMONFARED, Ayad

2016-01-01

Background: Due to their specific nature, such as high incidence, high intensity and direct involvement of all members of society, traffic injuries are of particular importance. Through a mega data, this study investigated the epidemiological aspects and depict current situation of road traffic injuries in Iran. Methods: Using legal medicine and traffic police data, deaths from road traffic injuries in men were predicted through determining the most appropriate model for death using time series statistical models; and then most important human factors associated with it in a period of 6 yr in Iran was analyzed using multi-nominal regression model. Results: The frequency of deaths from traffic injuries in the last seven years was 172,834 cases and the number of deaths at the accident scene was 42798 cases, of which 24.24% (41,971 cases) were recorded by the Traffic Police experts. Death rate from traffic injuries has been declined from 38 cases per 100,000 people in 2004 to 31 cases per 100,000 people between 2009 and 2010. Fatigue and sleepiness (AOR=10.36, 95% CI: 8.41–13.3) was the most significant human risk factors for death outcome in the urban and suburban traffic injuries. According to the predictions, the death rate is about 16488 (CI 95%, 8531–24364) for the year 2012. Conclusion: Despite all measures to prevent such injuries, even fatal injuries have still a high incidence. Intervention in the human risk factors field would be more effective due to their important roles in traffic injuries in Iran. PMID:27252920

Impact of road traffic emissions on tropospheric ozone in Europe for present day and future scenarios

NASA Astrophysics Data System (ADS)

Mertens, Mariano; Kerkweg, Astrid; Grewe, Volker; Jöckel, Patrick

2016-04-01

Road traffic is an important anthropogenic source of NOx, CO and non-methane hydrocarbons (NMHCs) which act as precursors for the formation of tropospheric ozone. The formation of ozone is highly non-linear. This means that the contribution of the road traffic sector cannot directly be derived from the amount of emitted species, because they are also determined by local emissions of other anthropogenic and natural sources. In addition, long range transport of precursors and ozone can play an important role in determining the local ozone budget. For a complete assessment of the impact of road traffic emissions it is therefore important to resolve both, local emissions and long range transport. This can be achieved by the use of the newly developed MECO(n) model system, which on-line couples the global chemistry-climate-model EMAC with the regional chemistry-climate-model COSMO-CLM/MESSy. Both models use the same chemical speciation. This allows a highly consistent model chain from the global to the local scale. To quantify the contribution of the road traffic emissions to tropospheric ozone we use an accounting system of the relevant reaction pathways of the different species from different sources (called tagging method). This tagging scheme is implemented consistently on all scales, allowing a direct comparison of the contributions. With this model configuration we investigate the impact of road traffic emissions to the tropospheric ozone budget in Europe. For the year 2008 we compare different emission scenarios and investigate the influence of both model and emission resolution. In addition, results of a mitigation scenario for the year 2030 are presented. They indicate that the contribution of the road traffic sector can be reduced by local reductions of emissions during summer. During winter the importance of long range transport increases. This can lead to increased contributions of the road traffic sector (e.g. by increased emissions in the US) even if local emissions are reduced.

The investigation of the lateral interaction effect's on traffic flow behavior under open boundaries

NASA Astrophysics Data System (ADS)

Bouadi, M.; Jetto, K.; Benyoussef, A.; El Kenz, A.

2017-11-01

In this paper, an open boundaries traffic flow system is studied by taking into account the lateral interaction with spatial defects. For a random defects distribution, if the vehicles velocities are weakly correlated, the traffic phases can be predicted by considering the corresponding inflow and outflow functions. Conversely, if the vehicles velocities are strongly correlated, a phase segregation appears inside the system's bulk which induces the maximum current appearance. Such velocity correlation depends mainly on the defects densities and the probabilities of lateral deceleration. However, for a compact defects distribution, the traffic phases are predictable by using the inflow in the system beginning, the inflow entering the defects zone and the outflow function.

Investigating the effects of the fixed and varying dispersion parameters of Poisson-gamma models on empirical Bayes estimates.

PubMed

Lord, Dominique; Park, Peter Young-Jin

2008-07-01

Traditionally, transportation safety analysts have used the empirical Bayes (EB) method to improve the estimate of the long-term mean of individual sites; to correct for the regression-to-the-mean (RTM) bias in before-after studies; and to identify hotspots or high risk locations. The EB method combines two different sources of information: (1) the expected number of crashes estimated via crash prediction models, and (2) the observed number of crashes at individual sites. Crash prediction models have traditionally been estimated using a negative binomial (NB) (or Poisson-gamma) modeling framework due to the over-dispersion commonly found in crash data. A weight factor is used to assign the relative influence of each source of information on the EB estimate. This factor is estimated using the mean and variance functions of the NB model. With recent trends that illustrated the dispersion parameter to be dependent upon the covariates of NB models, especially for traffic flow-only models, as well as varying as a function of different time-periods, there is a need to determine how these models may affect EB estimates. The objectives of this study are to examine how commonly used functional forms as well as fixed and time-varying dispersion parameters affect the EB estimates. To accomplish the study objectives, several traffic flow-only crash prediction models were estimated using a sample of rural three-legged intersections located in California. Two types of aggregated and time-specific models were produced: (1) the traditional NB model with a fixed dispersion parameter and (2) the generalized NB model (GNB) with a time-varying dispersion parameter, which is also dependent upon the covariates of the model. Several statistical methods were used to compare the fitting performance of the various functional forms. The results of the study show that the selection of the functional form of NB models has an important effect on EB estimates both in terms of estimated values, weight factors, and dispersion parameters. Time-specific models with a varying dispersion parameter provide better statistical performance in terms of goodness-of-fit (GOF) than aggregated multi-year models. Furthermore, the identification of hazardous sites, using the EB method, can be significantly affected when a GNB model with a time-varying dispersion parameter is used. Thus, erroneously selecting a functional form may lead to select the wrong sites for treatment. The study concludes that transportation safety analysts should not automatically use an existing functional form for modeling motor vehicle crashes without conducting rigorous analyses to estimate the most appropriate functional form linking crashes with traffic flow.

Analysis of the effect of older drivers’ driving behaviors on traffic flow based on a modified CA model

NASA Astrophysics Data System (ADS)

Jian, Mei-Ying; Shi, Jing; Liu, Yang

2016-09-01

As the global population ages, there are more and more older drivers on the road. The decline in driving performance of older drivers may influence the properties of traffic flow and safety. The purpose of this paper is to investigate the effect of older drivers’ driving behaviors on traffic flow. A modified cellular automaton (CA) model which takes driving behaviors of older drivers into account is proposed. The simulation results indicate that older drivers’ driving behaviors induce a reduction in traffic flow especially when the density is higher than 15 vehicles per km per lane and an increase in Lane-changing frequency. The analysis of stability shows that a number of disturbances could frequently emerge, be propagated and eventually dissipate in this modified model. The results also reflect that with the increase of older drivers on the road, the probability of the occurrence of rear-end collisions increases greatly and obviously. Furthermore, the value of acceleration influences the traffic flow and safety significantly. These results provide the theoretical basis and reference for the traffic management departments to develop traffic management measure in the aging society.

Computing Programs for Determining Traffic Flows from Roundabouts

NASA Astrophysics Data System (ADS)

Boroiu, A. A.; Tabacu, I.; Ene, A.; Neagu, E.; Boroiu, A.

2017-10-01

For modelling road traffic at the level of a road network it is necessary to specify the flows of all traffic currents at each intersection. These data can be obtained by direct measurements at the traffic light intersections, but in the case of a roundabout this is not possible directly and the literature as well as the traffic modelling software doesn’t offer ways to solve this issue. Two sets of formulas are proposed by which all traffic flows from the roundabouts with 3 or 4 arms are calculated based on the streams that can be measured. The objective of this paper is to develop computational programs to operate with these formulas. For each of the two sets of analytical relations, a computational program was developed in the Java operating language. The obtained results fully confirm the applicability of the calculation programs. The final stage for capitalizing these programs will be to make them web pages in HTML format, so that they can be accessed and used on the Internet. The achievements presented in this paper are an important step to provide a necessary tool for traffic modelling because these computational programs can be easily integrated into specialized software.

Relationship between road traffic accidents and conflicts recorded by drive recorders.

PubMed

Lu, Guangquan; Cheng, Bo; Kuzumaki, Seigo; Mei, Bingsong

2011-08-01

Road traffic conflicts can be used to estimate the probability of accident occurrence, assess road safety, or evaluate road safety programs if the relationship between road traffic accidents and conflicts is known. To this end, we propose a model for the relationship between road traffic accidents and conflicts recorded by drive recorders (DRs). DRs were installed in 50 cars in Beijing to collect records of traffic conflicts. Data containing 1366 conflicts were collected in 193 days. The hourly distributions of conflicts and accidents were used to model the relationship between accidents and conflicts. To eliminate time series and base number effects, we defined and used 2 parameters: average annual number of accidents per 10,000 vehicles per hour and average number of conflicts per 10,000 vehicles per hour. A model was developed to describe the relationship between the two parameters. If A(i) = average annual number of accidents per 10,000 vehicles per hour at hour i, and E(i) = average number of conflicts per 10,000 vehicles per hour at hour i, the relationship can be expressed as [Formula in text] (α>0, β>0). The average number of traffic accidents increases as the number of conflicts rises, but the rate of increase decelerates as the number of conflicts increases further. The proposed model can describe the relationship between road traffic accidents and conflicts in a simple manner. According to our analysis, the model fits the present data.

A novel interacting multiple model based network intrusion detection scheme

NASA Astrophysics Data System (ADS)

Xin, Ruichi; Venkatasubramanian, Vijay; Leung, Henry

2006-04-01

In today's information age, information and network security are of primary importance to any organization. Network intrusion is a serious threat to security of computers and data networks. In internet protocol (IP) based network, intrusions originate in different kinds of packets/messages contained in the open system interconnection (OSI) layer 3 or higher layers. Network intrusion detection and prevention systems observe the layer 3 packets (or layer 4 to 7 messages) to screen for intrusions and security threats. Signature based methods use a pre-existing database that document intrusion patterns as perceived in the layer 3 to 7 protocol traffics and match the incoming traffic for potential intrusion attacks. Alternately, network traffic data can be modeled and any huge anomaly from the established traffic pattern can be detected as network intrusion. The latter method, also known as anomaly based detection is gaining popularity for its versatility in learning new patterns and discovering new attacks. It is apparent that for a reliable performance, an accurate model of the network data needs to be established. In this paper, we illustrate using collected data that network traffic is seldom stationary. We propose the use of multiple models to accurately represent the traffic data. The improvement in reliability of the proposed model is verified by measuring the detection and false alarm rates on several datasets.

Long-term exposure to residential railway and road traffic noise and risk for diabetes in a Danish cohort.

PubMed

Roswall, Nina; Raaschou-Nielsen, Ole; Jensen, Steen Solvang; Tjønneland, Anne; Sørensen, Mette

2018-01-01

Road traffic noise exposure has been found associated with diabetes incidence. Evidence for an association between railway noise exposure is less clear, as large studies with detailed railway noise modelling are lacking. To investigate the association between residential railway noise and diabetes incidence, and to repeat previous analyses on road traffic noise and diabetes with longer follow-up time. Among 50,534 middle-aged Danes enrolled into the Diet, Cancer and Health cohort from 1993 to 97, we identified 5062 cases of incident diabetes during a median follow-up of 15.5 years. Present and historical residential addresses from 1987 to 2012 were found in national registries, and railway and road traffic noise (L den ) were modelled for all addresses, using the Nordic prediction method. We used Cox proportional hazard models to investigate the association between residential traffic noise over 1 and 5 years before diagnosis, and diabetes incidence. Hazard ratios (HRs) were calculated as crude and adjusted for potential confounders. We found no association between railway noise exposure and diabetes incidence among the 9527 persons exposed, regardless of exposure time-window: HR 0.99 (0.94-1.04) per 10dB for 5-year exposure in fully adjusted models. There was no effect modification by sex, road traffic noise, and education. We confirmed the previously found association between road traffic noise exposure and diabetes including 6 additional years of follow-up: HR 1.08 (1.04-1.13) per 10dB for 5-year exposure in fully adjusted models. The study does not suggest an association between residential railway noise exposure and diabetes incidence, but supports the finding of a direct association with residential road traffic noise. Copyright © 2017 Elsevier Inc. All rights reserved.

Probabilistic physical characteristics of phase transitions at highway bottlenecks: incommensurability of three-phase and two-phase traffic-flow theories.

PubMed

Kerner, Boris S; Klenov, Sergey L; Schreckenberg, Michael

2014-05-01

Physical features of induced phase transitions in a metastable free flow at an on-ramp bottleneck in three-phase and two-phase cellular automaton (CA) traffic-flow models have been revealed. It turns out that at given flow rates at the bottleneck, to induce a moving jam (F → J transition) in the metastable free flow through the application of a time-limited on-ramp inflow impulse, in both two-phase and three-phase CA models the same critical amplitude of the impulse is required. If a smaller impulse than this critical one is applied, neither F → J transition nor other phase transitions can occur in the two-phase CA model. We have found that in contrast with the two-phase CA model, in the three-phase CA model, if the same smaller impulse is applied, then a phase transition from free flow to synchronized flow (F → S transition) can be induced at the bottleneck. This explains why rather than the F → J transition, in the three-phase theory traffic breakdown at a highway bottleneck is governed by an F → S transition, as observed in real measured traffic data. None of two-phase traffic-flow theories incorporates an F → S transition in a metastable free flow at the bottleneck that is the main feature of the three-phase theory. On the one hand, this shows the incommensurability of three-phase and two-phase traffic-flow theories. On the other hand, this clarifies why none of the two-phase traffic-flow theories can explain the set of fundamental empirical features of traffic breakdown at highway bottlenecks.

Valiant load-balanced robust routing under hose model for WDM mesh networks

NASA Astrophysics Data System (ADS)

Zhang, Xiaoning; Li, Lemin; Wang, Sheng

2006-09-01

In this paper, we propose Valiant Load-Balanced robust routing scheme for WDM mesh networks under the model of polyhedral uncertainty (i.e., hose model), and the proposed routing scheme is implemented with traffic grooming approach. Our Objective is to maximize the hose model throughput. A mathematic formulation of Valiant Load-Balanced robust routing is presented and three fast heuristic algorithms are also proposed. When implementing Valiant Load-Balanced robust routing scheme to WDM mesh networks, a novel traffic-grooming algorithm called MHF (minimizing hop first) is proposed. We compare the three heuristic algorithms with the VPN tree under the hose model. Finally we demonstrate in the simulation results that MHF with Valiant Load-Balanced robust routing scheme outperforms the traditional traffic-grooming algorithm in terms of the throughput for the uniform/non-uniform traffic matrix under the hose model.

Disparities in Exposure to Automobile and Truck Traffic and Vehicle Emissions Near the Los Angeles–Long Beach Port Complex

PubMed Central

Li, Wei; Wu, Jun

2014-01-01

Objectives. We assessed how traffic and mobile-source air pollution impacts are distributed across racial/ethnic and socioeconomically diverse groups in port-adjacent communities in southern Los Angeles County, which may experience divergent levels of exposure to port-related heavy-duty diesel truck traffic because of existing residential and land use patterns. Methods. We used spatial regression techniques to assess the association of neighborhood racial/ethnic and socioeconomic composition with residential parcel-level traffic and vehicle-related fine particulate matter exposure after accounting for built environment and land use factors. Results. After controlling for factors associated with traffic generation, we found that a higher percentage of nearby Black and Asian/Pacific Islander residents was associated with higher exposure, a higher percentage of Hispanic residents was associated with higher traffic exposure but lower vehicle particulate matter exposure, and areas with lower socioeconomic status experienced lower exposure. Conclusions. Disparities in traffic and vehicle particulate matter exposure are nuanced depending on the exposure metric used, the distribution of the traffic and emissions, and pollutant dispersal patterns. Future comparative research is needed to assess potential disparities in other transportation and goods movement corridors. PMID:23678919

Disparities in exposure to automobile and truck traffic and vehicle emissions near the Los Angeles-Long Beach port complex.

PubMed

Houston, Douglas; Li, Wei; Wu, Jun

2014-01-01

We assessed how traffic and mobile-source air pollution impacts are distributed across racial/ethnic and socioeconomically diverse groups in port-adjacent communities in southern Los Angeles County, which may experience divergent levels of exposure to port-related heavy-duty diesel truck traffic because of existing residential and land use patterns. We used spatial regression techniques to assess the association of neighborhood racial/ethnic and socioeconomic composition with residential parcel-level traffic and vehicle-related fine particulate matter exposure after accounting for built environment and land use factors. After controlling for factors associated with traffic generation, we found that a higher percentage of nearby Black and Asian/Pacific Islander residents was associated with higher exposure, a higher percentage of Hispanic residents was associated with higher traffic exposure but lower vehicle particulate matter exposure, and areas with lower socioeconomic status experienced lower exposure. Disparities in traffic and vehicle particulate matter exposure are nuanced depending on the exposure metric used, the distribution of the traffic and emissions, and pollutant dispersal patterns. Future comparative research is needed to assess potential disparities in other transportation and goods movement corridors.

The willingness to pay of parties to traffic accidents for loss of productivity and consolation compensation.

PubMed

Jou, Rong-Chang; Chen, Tzu-Ying

2015-12-01

In this study, willingness to pay (WTP) for loss of productivity and consolation compensation by parties to traffic accidents is investigated using the Tobit model. In addition, WTP is compared to compensation determined by Taiwanese courts. The modelling results showed that variables such as education, average individual monthly income, traffic accident history, past experience of severe traffic accident injuries, the number of working days lost due to a traffic accident, past experience of accepting compensation for traffic accident-caused productivity loss and past experience of accepting consolation compensation caused by traffic accidents have a positive impact on WTP. In addition, average WTP for these two accident costs were obtained. We found that parties to traffic accidents were willing to pay more than 90% of the compensation determined by the court in the scenario of minor and moderate injuries. Parties were willing to pay approximately 80% of the compensation determined by the court for severe injuries, disability and fatality. Therefore, related agencies can use our study findings as the basis for determining the compensation that parties should pay for productivity losses caused by traffic accidents of different types. Copyright © 2015 Elsevier Ltd. All rights reserved.

A new macro model of traffic flow by incorporating both timid and aggressive driving behaviors

NASA Astrophysics Data System (ADS)

Peng, Guanghan; Qing, Li

2016-10-01

In this paper, a novel macro model is derived from car-following model by applying the relationship between the micro and macro variables by incorporating the timid and aggressive effects of optimal velocity on a single lane. Numerical simulation shows that the timid and aggressive macro model of traffic flow can correctly reproduce common evolution of shock, rarefaction waves and local cluster effects under small perturbation. Also, the results uncover that the aggressive effect can smoothen the front of the shock wave and the timid effect results in local press peak, which means that the timid effect hastens the process of congregation in the shock wave. The more timid traffic behaviors are, the smaller is the stable range. Furthermore, the research shows that the advantage of the aggressive effect over the timid one lies in the fact that the aggressive traffic behaviors can improve the stability of traffic flow with the consideration of incorporating timid and aggressive driving behaviors at the same time.

An extended continuum model considering optimal velocity change with memory and numerical tests

NASA Astrophysics Data System (ADS)

Qingtao, Zhai; Hongxia, Ge; Rongjun, Cheng

2018-01-01

In this paper, an extended continuum model of traffic flow is proposed with the consideration of optimal velocity changes with memory. The new model's stability condition and KdV-Burgers equation considering the optimal velocities change with memory are deduced through linear stability theory and nonlinear analysis, respectively. Numerical simulation is carried out to study the extended continuum model, which explores how optimal velocity changes with memory affected velocity, density and energy consumption. Numerical results show that when considering the effects of optimal velocity changes with memory, the traffic jams can be suppressed efficiently. Both the memory step and sensitivity parameters of optimal velocity changes with memory will enhance the stability of traffic flow efficiently. Furthermore, numerical results demonstrates that the effect of optimal velocity changes with memory can avoid the disadvantage of historical information, which increases the stability of traffic flow on road, and so it improve the traffic flow stability and minimize cars' energy consumptions.

GIS and Transportation Planning

DOT National Transportation Integrated Search

1998-09-16

Two main objectives of transportation planning are to simulate the current : traffic volume and to forecast the future traffic volume on a transportation : network. Traffic demand modeling typically consists of the following : tasks (1)defining traff...

Traffic forecasting report : 2007.

DOT National Transportation Integrated Search

2008-05-01

This is the sixth edition of the Traffic Forecasting Report (TFR). This edition of the TFR contains the latest (predominantly 2007) forecasting/modeling data as follows: : Functional class average traffic volume growth rates and trends : Vehi...

A Numerical Simulation of Traffic-Related Air Pollution Exposures in Urban Street Canyons

NASA Astrophysics Data System (ADS)

Liu, J.; Fu, X.; Tao, S.

2016-12-01

Urban street canyons are usually associated with intensive vehicle emissions. However, the high buildings successively along both sides of a street block the dispersion of traffic-generated air pollutants, which enhances human exposure and adversely affects human health. In this study, an urban scale traffic pollution dispersion model is developed with the consideration of street distribution, canyon geometry, background meteorology, traffic assignment, traffic emissions and air pollutant dispersion. Vehicle exhausts generated from traffic flows will first disperse inside a street canyon along the micro-scale wind field (generated by computational fluid dynamics (CFD) model) and then leave the street canyon and further disperse over the urban area. On the basis of this model, the effects of canyon geometry on the distribution of NOx and CO from traffic emissions were studied over the center of Beijing, China. We found that an increase of building height along the streets leads to higher pollution levels inside streets and lower pollution levels outside, resulting in higher domain-averaged concentrations over the area. In addition, street canyons with equal (or highly uneven) building heights on two sides of a street tend to lower the urban-scale air pollution concentrations at pedestrian level. Our results indicate that canyon geometry strongly influences human exposure to traffic pollutants in the populated urban area. Carefully planning street layout and canyon geometry in consideration of traffic demand as well as local weather pattern may significantly reduce the chances of unhealthy air being inhaled by urban residents.

Improving pedestrian facilities in congested urban areas: a case study of Chennai city

NASA Astrophysics Data System (ADS)

Subramanyam, B.; Prasanna Kumar, R.

2017-07-01

Traffic congestion and lack of public pedestrian space are some problems faced by most urban metropolises. Conventionally walking has been a mode of transportation in Indian cities. The percentage of pedestrians may vary from 16 to 57 depending upon the city. Encounters between vehicular traffic and pedestrian traffic are at its rise currently. Rapid industrialization and urbanization in India has resulted in neglecting of pedestrian facilities. Consequently pedestrian are at greater risk for their safety more especially in the commercial zones of large cities. A change in perspective spotlight will create a sense of awareness that the pedestrian traffic is also vital as the vehicular traffic. Soothing the traffic would moderately cut the driving expediency but the pedestrians will get a much safer and peaceful route to their terminuses. Safety and comfort are the two pans of a balance while considering the pedestrian traffic. Considering these aspects, this study deals a study in improving pedestrian facilities by analysing the existing skeleton of the selected locations. The adequacy of facility is checked based on IRC latest guidelines and counteractive measures are postulated.

A novel multisensor traffic state assessment system based on incomplete data.

PubMed

Zeng, Yiliang; Lan, Jinhui; Ran, Bin; Jiang, Yaoliang

2014-01-01

A novel multisensor system with incomplete data is presented for traffic state assessment. The system comprises probe vehicle detection sensors, fixed detection sensors, and traffic state assessment algorithm. First of all, the validity checking of the traffic flow data is taken as preprocessing of this method. And then a new method based on the history data information is proposed to fuse and recover the incomplete data. According to the characteristics of space complementary of data based on the probe vehicle detector and fixed detector, a fusion model of space matching is presented to estimate the mean travel speed of the road. Finally, the traffic flow data include flow, speed and, occupancy rate, which are detected between Beijing Deshengmen bridge and Drum Tower bridge, are fused to assess the traffic state of the road by using the fusion decision model of rough sets and cloud. The accuracy of experiment result can reach more than 98%, and the result is in accordance with the actual road traffic state. This system is effective to assess traffic state, and it is suitable for the urban intelligent transportation system.

A Novel Multisensor Traffic State Assessment System Based on Incomplete Data

PubMed Central

Zeng, Yiliang; Lan, Jinhui; Ran, Bin; Jiang, Yaoliang

2014-01-01

A novel multisensor system with incomplete data is presented for traffic state assessment. The system comprises probe vehicle detection sensors, fixed detection sensors, and traffic state assessment algorithm. First of all, the validity checking of the traffic flow data is taken as preprocessing of this method. And then a new method based on the history data information is proposed to fuse and recover the incomplete data. According to the characteristics of space complementary of data based on the probe vehicle detector and fixed detector, a fusion model of space matching is presented to estimate the mean travel speed of the road. Finally, the traffic flow data include flow, speed and, occupancy rate, which are detected between Beijing Deshengmen bridge and Drum Tower bridge, are fused to assess the traffic state of the road by using the fusion decision model of rough sets and cloud. The accuracy of experiment result can reach more than 98%, and the result is in accordance with the actual road traffic state. This system is effective to assess traffic state, and it is suitable for the urban intelligent transportation system. PMID:25162055

Traffic dynamics around weaving section influenced by accident: Cellular automata approach

NASA Astrophysics Data System (ADS)

Kong, Lin-Peng; Li, Xin-Gang; Lam, William H. K.

2015-07-01

The weaving section, as a typical bottleneck, is one source of vehicle conflicts and an accident-prone area. Traffic accident will block lanes and the road capacity will be reduced. Several models have been established to study the dynamics around traffic bottlenecks. However, little attention has been paid to study the complex traffic dynamics influenced by the combined effects of bottleneck and accident. This paper presents a cellular automaton model to characterize accident-induced traffic behavior around the weaving section. Some effective control measures are proposed and verified for traffic management under accident condition. The total flux as a function of inflow rates, the phase diagrams, the spatial-temporal diagrams, and the density and velocity profiles are presented to analyze the impact of accident. It was shown that the proposed control measures for weaving traffic can improve the capacity of weaving section under both normal and accident conditions; the accidents occurring on median lane in the weaving section are more inclined to cause traffic jam and reduce road capacity; the capacity of weaving section will be greatly reduced when the accident happens downstream the weaving section.

Dynamic route guidance strategy in a two-route pedestrian-vehicle mixed traffic flow system

NASA Astrophysics Data System (ADS)

Liu, Mianfang; Xiong, Shengwu; Li, Bixiang

2016-05-01

With the rapid development of transportation, traffic questions have become the major issue for social, economic and environmental aspects. Especially, during serious emergencies, it is very important to alleviate road traffic congestion and improve the efficiency of evacuation to reduce casualties, and addressing these problems has been a major task for the agencies responsible in recent decades. Advanced road guidance strategies have been developed for homogeneous traffic flows, or to reduce traffic congestion and enhance the road capacity in a symmetric two-route scenario. However, feedback strategies have rarely been considered for pedestrian-vehicle mixed traffic flows with variable velocities and sizes in an asymmetric multi-route traffic system, which is a common phenomenon in many developing countries. In this study, we propose a weighted road occupancy feedback strategy (WROFS) for pedestrian-vehicle mixed traffic flows, which considers the system equilibrium to ease traffic congestion. In order to more realistic simulating the behavior of mixed traffic objects, the paper adopted a refined and dynamic cellular automaton model (RDPV_CA model) as the update mechanism for pedestrian-vehicle mixed traffic flow. Moreover, a bounded rational threshold control was introduced into the feedback strategy to avoid some negative effect of delayed information and reduce. Based on comparisons with the two previously proposed strategies, the simulation results obtained in a pedestrian-vehicle traffic flow scenario demonstrated that the proposed strategy with a bounded rational threshold was more effective and system equilibrium, system stability were reached.

Neurobehavioral performance in adolescents is inversely associated with traffic exposure.

PubMed

Kicinski, Michal; Vermeir, Griet; Van Larebeke, Nicolas; Den Hond, Elly; Schoeters, Greet; Bruckers, Liesbeth; Sioen, Isabelle; Bijnens, Esmée; Roels, Harry A; Baeyens, Willy; Viaene, Mineke K; Nawrot, Tim S

2015-02-01

On the basis of animal research and epidemiological studies in children and elderly there is a growing concern that traffic exposure may affect the brain. The aim of our study was to investigate the association between traffic exposure and neurobehavioral performance in adolescents. We examined 606 adolescents. To model the exposure, we constructed a traffic exposure factor based on a biomarker of benzene (urinary trans,trans-muconic acid) and the amount of contact with traffic preceding the neurobehavioral examination (using distance-weighted traffic density and time spent in traffic). We used a Bayesian structural equation model to investigate the association between traffic exposure and three neurobehavioral domains: sustained attention, short-term memory, and manual motor speed. A one standard deviation increase in traffic exposure was associated with a 0.26 standard deviation decrease in sustained attention (95% credible interval: -0.02 to -0.51), adjusting for gender, age, smoking, passive smoking, level of education of the mother, socioeconomic status, time of the day, and day of the week. The associations between traffic exposure and the other neurobehavioral domains studied had the same direction but did not reach the level of statistical significance. The results remained consistent in the sensitivity analysis excluding smokers and passive smokers. The inverse association between sustained attention and traffic exposure was independent of the blood lead level. Our study in adolescents supports the recent findings in children and elderly suggesting that traffic exposure adversely affects the neurobehavioral function. Copyright © 2014 Elsevier Ltd. All rights reserved.

Towards Realistic Urban Traffic Experiments Using DFROUTER: Heuristic, Validation and Extensions.

PubMed

Zambrano-Martinez, Jorge Luis; Calafate, Carlos T; Soler, David; Cano, Juan-Carlos

2017-12-15

Traffic congestion is an important problem faced by Intelligent Transportation Systems (ITS), requiring models that allow predicting the impact of different solutions on urban traffic flow. Such an approach typically requires the use of simulations, which should be as realistic as possible. However, achieving high degrees of realism can be complex when the actual traffic patterns, defined through an Origin/Destination (O-D) matrix for the vehicles in a city, remain unknown. Thus, the main contribution of this paper is a heuristic for improving traffic congestion modeling. In particular, we propose a procedure that, starting from real induction loop measurements made available by traffic authorities, iteratively refines the output of DFROUTER, which is a module provided by the SUMO (Simulation of Urban MObility) tool. This way, it is able to generate an O-D matrix for traffic that resembles the real traffic distribution and that can be directly imported by SUMO. We apply our technique to the city of Valencia, and we then compare the obtained results against other existing traffic mobility data for the cities of Cologne (Germany) and Bologna (Italy), thereby validating our approach. We also use our technique to determine what degree of congestion is expectable if certain conditions cause additional traffic to circulate in the city, adopting both a uniform pattern and a hotspot-based pattern for traffic injection to demonstrate how to regulate the overall number of vehicles in the city. This study allows evaluating the impact of vehicle flow changes on the overall traffic congestion levels.

Impacts of the driver's bounded rationality on the traffic running cost under the car-following model

NASA Astrophysics Data System (ADS)

Tang, Tie-Qiao; Luo, Xiao-Feng; Liu, Kai

2016-09-01

The driver's bounded rationality has significant influences on the micro driving behavior and researchers proposed some traffic flow models with the driver's bounded rationality. However, little effort has been made to explore the effects of the driver's bounded rationality on the trip cost. In this paper, we use our recently proposed car-following model to study the effects of the driver's bounded rationality on his running cost and the system's total cost under three traffic running costs. The numerical results show that considering the driver's bounded rationality will enhance his each running cost and the system's total cost under the three traffic running costs.

The effect of model uncertainty on some optimal routing problems

NASA Technical Reports Server (NTRS)

Mohanty, Bibhu; Cassandras, Christos G.

1991-01-01

The effect of model uncertainties on optimal routing in a system of parallel queues is examined. The uncertainty arises in modeling the service time distribution for the customers (jobs, packets) to be served. For a Poisson arrival process and Bernoulli routing, the optimal mean system delay generally depends on the variance of this distribution. However, as the input traffic load approaches the system capacity the optimal routing assignment and corresponding mean system delay are shown to converge to a variance-invariant point. The implications of these results are examined in the context of gradient-based routing algorithms. An example of a model-independent algorithm using online gradient estimation is also included.

Future Air Traffic Growth and Schedule Model, Supplement

NASA Technical Reports Server (NTRS)

Kimmel, William M. (Technical Monitor); Smith, Jeremy C.; Dollyhigh, Samuel M.

2004-01-01

The Future Air Traffic Growth and Schedule Model was developed as an implementation of the Fratar algorithm to project future traffic flow between airports in a system and of then scheduling the additional flights to reflect current passenger time-of-travel preferences. The methodology produces an unconstrained future schedule from a current (or baseline) schedule and the airport operations growth rates. As an example of the use of the model, future schedules are projected for 2010 and 2022 for all flights arriving at, departing from, or flying between all continental United States airports that had commercial scheduled service for May 17, 2002. Inter-continental US traffic and airports are included and the traffic is also grown with the Fratar methodology to account for their arrivals and departures to the continental US airports. Input data sets derived from the Official Airline Guide (OAG) data and FAA Terminal Area Forecast (TAF) are included in the examples of the computer code execution.

Traffic Flow Density Distribution Based on FEM

NASA Astrophysics Data System (ADS)

Ma, Jing; Cui, Jianming

In analysis of normal traffic flow, it usually uses the static or dynamic model to numerical analyze based on fluid mechanics. However, in such handling process, the problem of massive modeling and data handling exist, and the accuracy is not high. Finite Element Method (FEM) is a production which is developed from the combination of a modern mathematics, mathematics and computer technology, and it has been widely applied in various domain such as engineering. Based on existing theory of traffic flow, ITS and the development of FEM, a simulation theory of the FEM that solves the problems existing in traffic flow is put forward. Based on this theory, using the existing Finite Element Analysis (FEA) software, the traffic flow is simulated analyzed with fluid mechanics and the dynamics. Massive data processing problem of manually modeling and numerical analysis is solved, and the authenticity of simulation is enhanced.

Nonlinear analysis of an improved continuum model considering headway change with memory

NASA Astrophysics Data System (ADS)

Cheng, Rongjun; Wang, Jufeng; Ge, Hongxia; Li, Zhipeng

2018-01-01

Considering the effect of headway changes with memory, an improved continuum model of traffic flow is proposed in this paper. By means of linear stability theory, the new model’s linear stability with the effect of headway changes with memory is obtained. Through nonlinear analysis, the KdV-Burgers equation is derived to describe the propagating behavior of traffic density wave near the neutral stability line. Numerical simulation is carried out to study the improved traffic flow model, which explores how the headway changes with memory affected each car’s velocity, density and energy consumption. Numerical results show that when considering the effects of headway changes with memory, the traffic jams can be suppressed efficiently. Furthermore, research results demonstrate that the effect of headway changes with memory can avoid the disadvantage of historical information, which will improve the stability of traffic flow and minimize car energy consumption.

[Modeling the vehicle pollution in the urban streets before and during the Beijing Olympic Games traffic control period].

PubMed

Wang, Ting; Xie, Shao-dong

2010-03-01

In order to investigate the vehicle pollution situation in the streets in Beijing and the abatement during the Olympic Games, the OSPM model was applied to calculate the concentrations of PM10, CO, NO2 and O3 inside the urban streets of Beijing before and during the Olympic traffic controlling period in July, 2008. The modeled concentrations before the traffic control are 146 micog/m3, 3.83 mg/m3, 114.4 microg/m3 and 4.71 x 10(-1), while after the traffic control are 112 microg/m3, 3.16 mg/m3, 102.4 microg/m3 and 5.31 x 10(-9) , with the reduction rates of 23.4%, 20.5%, 10.5% and -12.5%, respectively. The research on these concentration changes and the daily variations of the pollutants reveals: the concentration of PM10 is most influenced by the traffic control; the concentration of CO presents the most similar daily variation with the traffic flow; the reduction of NO2 concentration is limited, indicating the influence of other factors other than the traffic emission; the concentration of O3 increases after the traffic control, which means the traffic management measures can not abate the O3 pollution in the street. Furthermore, the comparison between the calculation results in different types of street canyons reveals that the fleet composition and street geometry impact the concentration changes. In a word, the vehicle pollution inside the streets of Beijing before the traffic control is relatively serious, as the concentrations of PM10, CO and NO2, all approach or exceed the Grade II National Air Quality Standard; the traffic control measures take effect in reducing the primary pollutants, but the secondary pollutants may increase after the traffic control.

Detailed emission profiles for on-road vehicles derived from ambient measurements during a windless traffic episode in Baltimore using a multi-model approach

NASA Astrophysics Data System (ADS)

Ke, Haohao; Ondov, John M.; Rogge, Wolfgang F.

2013-12-01

Composite chemical profiles of motor vehicle emissions were extracted from ambient measurements at a near-road site in Baltimore during a windless traffic episode in November, 2002, using four independent approaches, i.e., simple peak analysis, windless model-based linear regression, PMF, and UNMIX. Although the profiles are in general agreement, the windless-model-based profile treatment more effectively removes interference from non-traffic sources and is deemed to be more accurate for many species. In addition to abundances of routine pollutants (e.g., NOx, CO, PM2.5, EC, OC, sulfate, and nitrate), 11 particle-bound metals and 51 individual traffic-related organic compounds (including n-alkanes, PAHs, oxy-PAHs, hopanes, alkylcyclohexanes, and others) were included in the modeling.

Continuum modeling of cooperative traffic flow dynamics

NASA Astrophysics Data System (ADS)

Ngoduy, D.; Hoogendoorn, S. P.; Liu, R.

2009-07-01

This paper presents a continuum approach to model the dynamics of cooperative traffic flow. The cooperation is defined in our model in a way that the equipped vehicle can issue and receive a warning massage when there is downstream congestion. Upon receiving the warning massage, the (up-stream) equipped vehicle will adapt the current desired speed to the speed at the congested area in order to avoid sharp deceleration when approaching the congestion. To model the dynamics of such cooperative systems, a multi-class gas-kinetic theory is extended to capture the adaptation of the desired speed of the equipped vehicle to the speed at the downstream congested traffic. Numerical simulations are carried out to show the influence of the penetration rate of the equipped vehicles on traffic flow stability and capacity in a freeway.

Membrane traffic and muscle: lessons from human disease.

PubMed

Dowling, James J; Gibbs, Elizabeth M; Feldman, Eva L

2008-07-01

Like all mammalian tissues, skeletal muscle is dependent on membrane traffic for proper development and homeostasis. This fact is underscored by the observation that several human diseases of the skeletal muscle are caused by mutations in gene products of the membrane trafficking machinery. An examination of these diseases and the proteins that underlie them is instructive both in terms of determining disease pathogenesis and of understanding the normal aspects of muscle biology regulated by membrane traffic. This review highlights our current understanding of the trafficking genes responsible for human myopathies.

Congestion and communication in confined ant traffic

NASA Astrophysics Data System (ADS)

Gravish, Nick; Gold, Gregory; Zangwill, Andrew; Goodisman, Michael A. D.; Goldman, Daniel I.

2014-03-01

Many social animals move and communicate within confined spaces. In subterranean fire ants Solenopsis invicta, mobility within crowded nest tunnels is important for resource and information transport. Within confined tunnels, communication and traffic flow are at odds: trafficking ants communicate through tactile interactions while stopped, yet ants that stop to communicate impose physical obstacles on the traffic. We monitor the bi-directional flow of fire ant workers in laboratory tunnels of varied diameter D. The persistence time of communicating ant aggregations, τ, increases approximately linearly with the number of participating ants, n. The sensitivity of traffic flow increases as D decreases and diverges at a minimum diameter, Dc. A cellular automata model incorporating minimal traffic features--excluded volume and communication duration--reproduces features of the experiment. From the model we identify a competition between information transfer and the need to maintain jam-free traffic flow. We show that by balancing information transfer and traffic flow demands, an optimum group strategy exists which maximizes information throughput. We acknowledge funding from NSF PoLS #0957659 and #PHY-1205878.

Stability analysis of feedforward anticipation optimal flux difference in traffic lattice hydrodynamic theory

NASA Astrophysics Data System (ADS)

Sun, Di-Hua; Zhang, Geng; Zhao, Min; Cheng, Sen-Lin; Cao, Jian-Dong

2018-03-01

Recently, the influence of driver's individual behaviors on traffic stability is research hotspot with the fasting developing transportation cyber-physical systems. In this paper, a new traffic lattice hydrodynamic model is proposed with consideration of driver's feedforward anticipation optimal flux difference. The neutral stability condition of the new model is obtained through linear stability analysis theory. The results show that the stable region will be enlarged on the phase diagram when the feedforward anticipation optimal flux difference effect is taken into account. In order to depict traffic jamming transition properties theoretically, the mKdV equation near the critical point is derived via nonlinear reductive perturbation method. The propagation behavior of traffic density waves can be described by the kink-antikink solution of the mKdV equation. Numerical simulations are conducted to verify the analytical results and all the results confirms that traffic stability can be enhanced significantly by considering the feedforward anticipation optimal flux difference in traffic lattice hydrodynamic theory.

Physics of automated driving in framework of three-phase traffic theory.

PubMed

Kerner, Boris S

2018-04-01

We have revealed physical features of automated driving in the framework of the three-phase traffic theory for which there is no fixed time headway to the preceding vehicle. A comparison with the classical model approach to automated driving for which an automated driving vehicle tries to reach a fixed (desired or "optimal") time headway to the preceding vehicle has been made. It turns out that automated driving in the framework of the three-phase traffic theory can exhibit the following advantages in comparison with the classical model of automated driving: (i) The absence of string instability. (ii) Considerably smaller speed disturbances at road bottlenecks. (iii) Automated driving vehicles based on the three-phase theory can decrease the probability of traffic breakdown at the bottleneck in mixed traffic flow consisting of human driving and automated driving vehicles; on the contrary, even a single automated driving vehicle based on the classical approach can provoke traffic breakdown at the bottleneck in mixed traffic flow.

Physics of automated driving in framework of three-phase traffic theory

NASA Astrophysics Data System (ADS)

Kerner, Boris S.

2018-04-01

We have revealed physical features of automated driving in the framework of the three-phase traffic theory for which there is no fixed time headway to the preceding vehicle. A comparison with the classical model approach to automated driving for which an automated driving vehicle tries to reach a fixed (desired or "optimal") time headway to the preceding vehicle has been made. It turns out that automated driving in the framework of the three-phase traffic theory can exhibit the following advantages in comparison with the classical model of automated driving: (i) The absence of string instability. (ii) Considerably smaller speed disturbances at road bottlenecks. (iii) Automated driving vehicles based on the three-phase theory can decrease the probability of traffic breakdown at the bottleneck in mixed traffic flow consisting of human driving and automated driving vehicles; on the contrary, even a single automated driving vehicle based on the classical approach can provoke traffic breakdown at the bottleneck in mixed traffic flow.

Validation of air traffic controller workload models

DOT National Transportation Integrated Search

1979-09-01

During the past several years, computer models have been developed for off-site : estimat ion of control ler's workload. The inputs to these models are audio and : digital data normally recorded at an Air Route Traffic Control Center (ARTCC). : This ...

Improving traffic signal management and operations : a basic service model.

DOT National Transportation Integrated Search

2009-12-01

This report provides a guide for achieving a basic service model for traffic signal management and : operations. The basic service model is based on simply stated and defensible operational objectives : that consider the staffing level, expertise and...

Evaluation of origin-destination matrix estimation techniques to support aspects of traffic modeling.

DOT National Transportation Integrated Search

2014-05-01

Travel demand forecasting models are used to predict future traffic volumes to evaluate : roadway improvement alternatives. Each of the metropolitan planning organizations (MPO) in : Alabama maintains a travel demand model to support planning efforts...

Cloud-based large-scale air traffic flow optimization

NASA Astrophysics Data System (ADS)

Cao, Yi

The ever-increasing traffic demand makes the efficient use of airspace an imperative mission, and this paper presents an effort in response to this call. Firstly, a new aggregate model, called Link Transmission Model (LTM), is proposed, which models the nationwide traffic as a network of flight routes identified by origin-destination pairs. The traversal time of a flight route is assumed to be the mode of distribution of historical flight records, and the mode is estimated by using Kernel Density Estimation. As this simplification abstracts away physical trajectory details, the complexity of modeling is drastically decreased, resulting in efficient traffic forecasting. The predicative capability of LTM is validated against recorded traffic data. Secondly, a nationwide traffic flow optimization problem with airport and en route capacity constraints is formulated based on LTM. The optimization problem aims at alleviating traffic congestions with minimal global delays. This problem is intractable due to millions of variables. A dual decomposition method is applied to decompose the large-scale problem such that the subproblems are solvable. However, the whole problem is still computational expensive to solve since each subproblem is an smaller integer programming problem that pursues integer solutions. Solving an integer programing problem is known to be far more time-consuming than solving its linear relaxation. In addition, sequential execution on a standalone computer leads to linear runtime increase when the problem size increases. To address the computational efficiency problem, a parallel computing framework is designed which accommodates concurrent executions via multithreading programming. The multithreaded version is compared with its monolithic version to show decreased runtime. Finally, an open-source cloud computing framework, Hadoop MapReduce, is employed for better scalability and reliability. This framework is an "off-the-shelf" parallel computing model that can be used for both offline historical traffic data analysis and online traffic flow optimization. It provides an efficient and robust platform for easy deployment and implementation. A small cloud consisting of five workstations was configured and used to demonstrate the advantages of cloud computing in dealing with large-scale parallelizable traffic problems.

Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata

PubMed Central

Chen, Yangzhou; Guo, Yuqi; Wang, Ying

2017-01-01

In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by embedding the Cell Transmission Model (CTM) into the DGHA. With a modeling procedure, we adopt a dual digraph of road network structure to describe the road topology, use linear hybrid automata to describe multi-modes of dynamic densities in road segments and transform the nonlinear expressions of the transmitted traffic flow between two road segments into piecewise linear functions in terms of multi-mode switchings. This modeling procedure is modularized and rule-based, and thus is easily-extensible with the help of a combination algorithm for the dynamics of traffic flow. It can describe the dynamics of traffic flow over an urban freeway network with arbitrary topology structures and sizes. Next we analyze mode types and number in the model of the whole freeway network, and deduce a Piecewise Affine Linear System (PWALS) model. Furthermore, based on the PWALS model, a multi-mode switched state observer is designed to estimate the traffic densities of the freeway network, where a set of observer gain matrices are computed by using the Lyapunov function approach. As an example, we utilize the PWALS model and the corresponding switched state observer to traffic flow over Beijing third ring road. In order to clearly interpret the principle of the proposed method and avoid computational complexity, we adopt a simplified version of Beijing third ring road. Practical application for a large-scale road network will be implemented by decentralized modeling approach and distributed observer designing in the future research. PMID:28353664

Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata.

PubMed

Chen, Yangzhou; Guo, Yuqi; Wang, Ying

2017-03-29

In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by embedding the Cell Transmission Model (CTM) into the DGHA. With a modeling procedure, we adopt a dual digraph of road network structure to describe the road topology, use linear hybrid automata to describe multi-modes of dynamic densities in road segments and transform the nonlinear expressions of the transmitted traffic flow between two road segments into piecewise linear functions in terms of multi-mode switchings. This modeling procedure is modularized and rule-based, and thus is easily-extensible with the help of a combination algorithm for the dynamics of traffic flow. It can describe the dynamics of traffic flow over an urban freeway network with arbitrary topology structures and sizes. Next we analyze mode types and number in the model of the whole freeway network, and deduce a Piecewise Affine Linear System (PWALS) model. Furthermore, based on the PWALS model, a multi-mode switched state observer is designed to estimate the traffic densities of the freeway network, where a set of observer gain matrices are computed by using the Lyapunov function approach. As an example, we utilize the PWALS model and the corresponding switched state observer to traffic flow over Beijing third ring road. In order to clearly interpret the principle of the proposed method and avoid computational complexity, we adopt a simplified version of Beijing third ring road. Practical application for a large-scale road network will be implemented by decentralized modeling approach and distributed observer designing in the future research.

Zone clearance in an infinite TASEP with a step initial condition

NASA Astrophysics Data System (ADS)

Cividini, Julien; Appert-Rolland, Cécile

2017-06-01

The TASEP is a paradigmatic model of out-of-equilibrium statistical physics, for which many quantities have been computed, either exactly or by approximate methods. In this work we study two new kinds of observables that have some relevance in biological or traffic models. They represent the probability for a given clearance zone of the lattice to be empty (for the first time) at a given time, starting from a step density profile. Exact expressions are obtained for single-time quantities, while more involved history-dependent observables are studied by Monte Carlo simulation, and partially predicted by a phenomenological approach.

Modeling the effect of microscopic driving behaviors on Kerner's time-delayed traffic breakdown at traffic signal using cellular automata

NASA Astrophysics Data System (ADS)

Wang, Yang; Chen, Yan-Yan

2016-12-01

The signalized traffic is considerably complex due to the fact that various driving behaviors have emerged to respond to traffic signals. However, the existing cellular automaton models take the signal-vehicle interactions into account inadequately, resulting in a potential risk that vehicular traffic flow dynamics may not be completely explored. To remedy this defect, this paper proposes a more realistic cellular automaton model by incorporating a number of the driving behaviors typically observed when the vehicles are approaching a traffic light. In particular, the anticipatory behavior proposed in this paper is realized with a perception factor designed by considering the vehicle speed implicitly and the gap to its preceding vehicle explicitly. Numerical simulations have been performed based on a signal controlled road which is partitioned into three sections according to the different reactions of drivers. The effects of microscopic driving behaviors on Kerner's time-delayed traffic breakdown at signal (Kerner 2011, 2013) have been investigated with the assistance of spatiotemporal pattern and trajectory analysis. Furthermore, the contributions of the driving behaviors on the traffic breakdown have been statistically examined. Finally, with the activation of the anticipatory behavior, the influences of the other driving behaviors on the formation of platoon have been investigated in terms of the number of platoons, the averaged platoon size, and the averaged flow rate.

The road traffic crashes as a neglected public health concern; an observational study from Iranian population.

PubMed

Bakhtiyari, Mahmood; Delpisheh, Ali; Monfared, Ayad Bahadori; Kazemi-Galougahi, Mohammad Hassan; Mehmandar, Mohammad Reza; Riahi, Mohammad; Salehi, Masoud; Mansournia, Mohammad Ali

2015-01-01

Traffic crashes are multifactorial events caused by human factors, technical issues, and environmental conditions. The present study aimed to determine the role of human factors in traffic crashes in Iran using the proportional odds regression model. The database of all traffic crashes in Iran in 2010 (n = 592, 168) registered through the "COM.114" police forms was investigated. Human risk factors leading to traffic crashes were determined and the odds ratio (OR) of each risk factor was estimated using an ordinal regression model and adjusted for potential confounding factors such as age, gender, and lighting status within and outside of cities. The drivers' mean age ± standard deviation was 34.1 ± 14.0 years. The most prevalent risk factors leading to death within cities were disregarding traffic rules and regulations (45%), driver rushing (31%), and alcohol consumption (12.3%). Using the proportional odds regression model, alcohol consumption was the most significant human risk factor in traffic crashes within cities (OR = 6.5, 95% confidence interval [CI], 4.88-8.65) and outside of cities (OR = 1.73, 95% CI, 1.22-3.29). Public health strategies and preventive policies should be focused on more common human risk factors such as disregarding traffic rules and regulations, drivers' rushing, and alcohol consumption due to their greater population attributable fraction and more intuitive impacts on society.

14 CFR 91.227 - Automatic Dependent Surveillance-Broadcast (ADS-B) Out equipment performance requirements.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 14 Aeronautics and Space 2 2013-01-01 2013-01-01 false Automatic Dependent Surveillance-Broadcast... Dependent Surveillance-Broadcast (ADS-B) Out equipment performance requirements. (a) Definitions. For the..., Extended Squitter Automatic Dependent Surveillance-Broadcast (ADS-B) and Traffic Information Service...

14 CFR 91.227 - Automatic Dependent Surveillance-Broadcast (ADS-B) Out equipment performance requirements.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 14 Aeronautics and Space 2 2012-01-01 2012-01-01 false Automatic Dependent Surveillance-Broadcast... Dependent Surveillance-Broadcast (ADS-B) Out equipment performance requirements. (a) Definitions. For the..., Extended Squitter Automatic Dependent Surveillance-Broadcast (ADS-B) and Traffic Information Service...

14 CFR 91.227 - Automatic Dependent Surveillance-Broadcast (ADS-B) Out equipment performance requirements.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 14 Aeronautics and Space 2 2014-01-01 2014-01-01 false Automatic Dependent Surveillance-Broadcast... Dependent Surveillance-Broadcast (ADS-B) Out equipment performance requirements. (a) Definitions. For the..., Extended Squitter Automatic Dependent Surveillance-Broadcast (ADS-B) and Traffic Information Service...

Transition Characteristic Analysis of Traffic Evolution Process for Urban Traffic Network

PubMed Central

Chen, Hong; Li, Yang

2014-01-01

The characterization of the dynamics of traffic states remains fundamental to seeking for the solutions of diverse traffic problems. To gain more insights into traffic dynamics in the temporal domain, this paper explored temporal characteristics and distinct regularity in the traffic evolution process of urban traffic network. We defined traffic state pattern through clustering multidimensional traffic time series using self-organizing maps and construct a pattern transition network model that is appropriate for representing and analyzing the evolution progress. The methodology is illustrated by an application to data flow rate of multiple road sections from Network of Shenzhen's Nanshan District, China. Analysis and numerical results demonstrated that the methodology permits extracting many useful traffic transition characteristics including stability, preference, activity, and attractiveness. In addition, more information about the relationships between these characteristics was extracted, which should be helpful in understanding the complex behavior of the temporal evolution features of traffic patterns. PMID:24982969

Effects of truck traffic on crash injury severity on rural highways in Wyoming using Bayesian binary logit models.

PubMed

Ahmed, Mohamed M; Franke, Rebecca; Ksaibati, Khaled; Shinstine, Debbie S

2018-08-01

Roadway safety is an integral part of a functioning infrastructure. A major use of the highway system is the transport of goods. The United States has experienced constant growth in the amount of freight transported by truck in the last few years. Wyoming is experiencing a large increase in truck traffic on its local and county roads due to an increase in oil and gas production. This study explores the involvement of heavy trucks in crashes and their significance as a predictor of crash severity and addresses the effect that large truck traffic is having on the safety of roadways for various road classifications. Studies have been done on the factors involved in and the causation of heavy truck crashes, but none address the causation and effect of roadway classifications on truck crashes. Binary Logit Models (BLM) with Bayesian inferences were utilized to classify heavy truck involvement in severe and non-severe crashes using ten years (2002-2011) of historical crash data in the State of Wyoming. From the final main effects model, various interactions proved to be significant in predicting the severity of crashes and varied depending on the roadway classification. The results indicated the odds of a severe crash increase to 2.3 and 4.5 times when a heavy truck is involved on state and interstate highways respectively. The severity of crashes is significantly increased when road conditions were not clear, icy, and during snowy weather conditions. Copyright © 2018 Elsevier Ltd. All rights reserved.

Models of Weather Impact on Air Traffic

NASA Technical Reports Server (NTRS)

Kulkarni, Deepak; Wang, Yao

2017-01-01

Flight delays have been a serious problem in the national airspace system costing about $30B per year. About 70 of the delays are attributed to weather and upto two thirds of these are avoidable. Better decision support tools would reduce these delays and improve air traffic management tools. Such tools would benefit from models of weather impacts on the airspace operations. This presentation discusses use of machine learning methods to mine various types of weather and traffic data to develop such models.

Simulation of traffic control signal systems

NASA Technical Reports Server (NTRS)

Connolly, P. J.; Concannon, P. A.; Ricci, R. C.

1974-01-01

In recent years there has been considerable interest in the development and testing of control strategies for networks of urban traffic signal systems by simulation. Simulation is an inexpensive and timely method for evaluating the effect of these traffic control strategies since traffic phenomena are too complex to be defined by analytical models and since a controlled experiment may be hazardous, expensive, and slow in producing meaningful results. This paper describes the application of an urban traffic corridor program, to evaluate the effectiveness of different traffic control strategies for the Massachusetts Avenue TOPICS Project.

Using recorded sound spectra profile as input data for real-time short-term urban road-traffic-flow estimation.

PubMed

Torija, Antonio J; Ruiz, Diego P

2012-10-01

Road traffic has a heavy impact on the urban sound environment, constituting the main source of noise and widely dominating its spectral composition. In this context, our research investigates the use of recorded sound spectra as input data for the development of real-time short-term road traffic flow estimation models. For this, a series of models based on the use of Multilayer Perceptron Neural Networks, multiple linear regression, and the Fisher linear discriminant were implemented to estimate road traffic flow as well as to classify it according to the composition of heavy vehicles and motorcycles/mopeds. In view of the results, the use of the 50-400 Hz and 1-2.5 kHz frequency ranges as input variables in multilayer perceptron-based models successfully estimated urban road traffic flow with an average percentage of explained variance equal to 86%, while the classification of the urban road traffic flow gave an average success rate of 96.1%. Copyright © 2012 Elsevier B.V. All rights reserved.

Review of Airport Ground Traffic Models Including an Evaluation of the ASTS Computer Program

DOT National Transportation Integrated Search

1972-12-01

The report covers an evaluation of Airport Ground Traffic models for the purpose of simulating an Autonomous Local Intersection Controller. All known models were reviewed and a detailed study was performed on the two in-house models the ASTS and ROSS...

Motorcycles entering from access points and merging with traffic on primary roads in Malaysia: behavioral and road environment influence on the occurrence of traffic conflicts.

PubMed

Abdul Manan, Muhammad Marizwan

2014-09-01

This paper uses data from an observational study, conducted at access points in straight sections of primary roads in Malaysia in 2012, to investigate the effects of motorcyclists' behavior and road environment attributes on the occurrence of serious traffic conflicts involving motorcyclists entering primary roads via access points. In order to handle the unobserved heterogeneity in the small sample data size, this study applies mixed effects logistic regression with multilevel bootstrapping. Two statistically significant models (Model 2 and Model 3) are produced, with 2 levels of random effect parameters, i.e. motorcyclists' attributes and behavior at Level 1, and road environment attributes at Level 2. Among all the road environment attributes tested, the traffic volume and the speed limit are found to be statistically significant, only contributing to 26-29% of the variations affecting the traffic conflict outcome. The implication is that 71-74% of the unmeasured or undescribed attributes and behavior of motorcyclists still have an importance in predicting the outcome: a serious traffic conflict. As for the fixed effect parameters, both models show that the risk of motorcyclists being involved in a serious traffic conflict is 2-4 times more likely if they accept a shorter gap to a single approaching vehicle (time lag <4s) and in between two vehicles (time gap <4s) when entering the primary road from the access point. A road environment factor, such as a narrow lane (seen in Model 2), and a behavioral factor, such as stopping at the stop line (seen in Model 3), also influence the occurrence of a serious traffic conflict compared to those entering into a wider lane road and without stopping at the stop line, respectively. A discussion of the possible reasons for this seemingly strange result, including a recommendation for further research, concludes the paper. Copyright © 2014 Elsevier Ltd. All rights reserved.

Assessing crash risk considering vehicle interactions with trucks using point detector data.

PubMed

Hyun, Kyung Kate; Jeong, Kyungsoo; Tok, Andre; Ritchie, Stephen G

2018-03-12

Trucks have distinct driving characteristics in general traffic streams such as lower speeds and limitations in acceleration and deceleration. As a consequence, vehicles keep longer headways or frequently change lane when they follow a truck, which is expected to increase crash risk. This study introduces several traffic measures at the individual vehicle level to capture vehicle interactions between trucks and non-trucks and analyzed how the measures affect crash risk under different traffic conditions. The traffic measures were developed using headways obtained from Inductive Loop Detectors (ILDs). In addition, a truck detection algorithm using a Gaussian Mixture (GM) model was developed to identify trucks and to estimate truck exposure from ILD data. Using the identified vehicle types from the GM model, vehicle interaction metrics were categorized into three groups based on the combination of leading and following vehicle types. The effects of the proposed traffic measures on crash risk were modeled in two different cases of prior- and non-crash using a case-control approach utilizing a conditional logistic regression. Results showed that the vehicle interactions between the leading and following vehicle types were highly associated with crash risk, and further showed different impacts on crash risk by traffic conditions. Specifically, crashes were more likely to occur when a truck following a non-truck had shorter average headway but greater headway variance in heavy traffic while a non-truck following a truck had greater headway variance in light traffic. This study obtained meaningful conclusions that vehicle interactions involved with trucks were significantly related to the crash likelihood rather than the measures that estimate average traffic condition such as total volume or average headway of the traffic stream. Copyright © 2018 Elsevier Ltd. All rights reserved.

A new model to improve aggregate air traffic demand predictions

DOT National Transportation Integrated Search

2007-08-20

Federal Aviation Administration (FAA) air traffic flow management (TFM) : decision-making is based primarily on a comparison of predictions of traffic demand and : available capacity at various National Airspace System (NAS) elements such as airports...

Model performance specifications for police traffic radar devices

DOT National Transportation Integrated Search

1982-03-01

This report provides information about all of the research work regarding police traffic radar completed by the National Bureau of Standards (NBS) under an Inter-Agency Agreement with the National Highway Traffic Safety Administration (NHTSA). Chapte...

Traffic prediction using wireless cellular networks : final report.

DOT National Transportation Integrated Search

2016-03-01

The major objective of this project is to obtain traffic information from existing wireless : infrastructure. : In this project freeway traffic is identified and modeled using data obtained from existing : wireless cellular networks. Most of the prev...

Querying and Extracting Timeline Information from Road Traffic Sensor Data

PubMed Central

Imawan, Ardi; Indikawati, Fitri Indra; Kwon, Joonho; Rao, Praveen

2016-01-01

The escalation of traffic congestion in urban cities has urged many countries to use intelligent transportation system (ITS) centers to collect historical traffic sensor data from multiple heterogeneous sources. By analyzing historical traffic data, we can obtain valuable insights into traffic behavior. Many existing applications have been proposed with limited analysis results because of the inability to cope with several types of analytical queries. In this paper, we propose the QET (querying and extracting timeline information) system—a novel analytical query processing method based on a timeline model for road traffic sensor data. To address query performance, we build a TQ-index (timeline query-index) that exploits spatio-temporal features of timeline modeling. We also propose an intuitive timeline visualization method to display congestion events obtained from specified query parameters. In addition, we demonstrate the benefit of our system through a performance evaluation using a Busan ITS dataset and a Seattle freeway dataset. PMID:27563900

A research of the community’s opening to the outside world

NASA Astrophysics Data System (ADS)

Xu, Lan; Liu, Xiangzhuo

2017-03-01

Closed residential areas, called community, the traffic network and result in various degrees of traffic congestion such as amputating, dead ends and T-shaped roads. In order to reveal the mechanism of the congestion, establish an effective evaluation index system and finally provide theoretical basis for the study of traffic congestion, we have done researches on factors for traffic congestion and have established a scientific evaluation index system combining experiences home and abroad, based on domestic congestion status. Firstly, we analyse the traffic network as the entry point, and then establish the evaluation model of road capacity with the method of AHP index system. Secondly, we divide the condition of urban congestion into 5 levels from congestion to smoothness. Besides, with VISSIM software, simulations about traffic capacity before and after community opening are carried out. Finally, we provide forward reasonable suggestions upon the combination of models and reality.

Research on Closed Residential Area Based on Balanced Distribution Theory

NASA Astrophysics Data System (ADS)

Lan, Si; Fang, Ni; Lin, Hai Peng; Ye, Shi Qi

2018-06-01

With the promotion of the street system, residential quarters and units of the compound gradually open. In this paper, the relationship between traffic flow and traffic flow is established for external roads, and the road resistance model is established by internal roads. We propose a balanced distribution model from the two aspects of road opening conditions and traffic flow inside and outside the district, and quantitatively analyze the impact of the opening and closing on the surrounding roads. Finally, it puts forward feasible suggestions to improve the traffic situation and optimize the network structure.

Road Traffic Noise Pollution Analysis for Cernavoda City

NASA Astrophysics Data System (ADS)

Manea, L.; Manea, A.; Florea, D.; Tarulescu, S.

2017-10-01

In the present paper was studied the noise pollution in Cernavodă city. The noise measurements were made for nine intersections from different city areas. Noise measurements were taken for three chosen routes with high population density, heavy traffic, commercial and residential buildings. Average, maximum and minimum values were collected and compared with standards. The impact of road traffic noise on the community depends on various factors such as road location and design, land use planning measures, building design, traffic composition, driver behaviour and the relief. In the study area 9 locations are identified to measure noise level. By using sound level meter noise levels are measured at different peak sessions i.e. morning, afternoon and evening. The presented values were collected for evening rush hour.

Oceanic Situational Awareness over the North Atlantic Corridor

NASA Technical Reports Server (NTRS)

Welch, Bryan; Greenfield, Israel

2005-01-01

Air traffic control (ATC) mandated, aircraft separations over the oceans impose a limitation on traffic capacity for a given corridor, given the projected traffic growth over the oceanic domain. The separations result from a lack of acceptable situational awareness over oceans where radar position updates are not available. This study considers the use of Automatic Dependent Surveillance (ADS) data transmitted over a commercial satellite communications system as an approach to provide ATC with the needed situational awareness and thusly allow for reduced aircraft separations. This study uses Federal Aviation Administration data from a single day for the North Atlantic Corridor to analyze traffic loading to be used as a benchmark against which to compare several approaches for coordinating data transmissions from the aircraft to the satellites.

Oceanic Situational Awareness Over the Pacific Corridor

NASA Technical Reports Server (NTRS)

Welch, Bryan; Greenfeld, Israel

2005-01-01

Air traffic control (ATC) mandated, aircraft separations over the oceans impose a limitation on traffic capacity for a given corridor, given the projected traffic growth over the Pacific Ocean. The separations result from a lack of acceptable situational awareness over oceans where radar position updates are not available. This study considers the use of Automatic Dependent Surveillance (ADS) data transmitted over a commercial satellite communications system as an approach to provide ATC with the needed situational awareness and thusly allow for reduced aircraft separations. This study uses Federal Aviation Administration data from a single day for the Pacific Corridor to analyze traffic loading to be used as a benchmark against which to compare several approaches for coordinating data transmissions from the aircraft to the satellites.

Oceanic Situational Awareness Over the Gulf of Mexico

NASA Technical Reports Server (NTRS)

Welch, Bryan; Greenfeld, Israel

2005-01-01

Air traffic control (ATC) mandated, aircraft separations over the oceans impose a limitation on traffic capacity for a given corridor, given the projected traffic growth over the Gulf of Mexico. The separations result from a lack of acceptable situational awareness over oceans where radar position updates are not available. This study considers the use of Automatic Dependent Surveillance (ADS) data transmitted over a commercial satellite communications system as an approach to provide ATC with the needed situational awareness and thusly allow for reduced aircraft separations. This study uses Federal Aviation Administration data from a single day for the Gulf of Mexico to analyze traffic loading to be used as a benchmark against which to compare several approaches for coordinating data transmissions from the aircraft to the satellites.

A generic approach for examining the effectiveness of traffic control devices in school zones.

PubMed

Zhao, Xiaohua; Li, Jiahui; Ding, Han; Zhang, Guohui; Rong, Jian

2015-09-01

The effectiveness and performance of traffic control devices in school zones have been impacted significantly by many factors, such as driver behavioral attributes, roadway geometric features, environmental characteristics, weather and visibility conditions, region-wide traffic regulations and policies, control modes, etc. When deploying traffic control devices in school zones, efforts are needed to clarify: (1) whether traffic control device installation is warranted; and (2) whether other device effectively complements this traffic control device and strengthens its effectiveness. In this study, a generic approach is developed to examine and evaluate the effectiveness of various traffic control devices deployed in school zones through driving simulator-based experiments. A Traffic Control Device Selection Model (TCDSM) is developed and two representative school zones are selected as the testbed in Beijing for driving simulation implementation to enhance its applicability. Statistical analyses are conducted to extract the knowledge from test data recorded by a driving simulator. Multiple measures of effectiveness (MOEs) are developed and adopted including average speed, relative speed difference, and standard deviation of acceleration for traffic control device performance quantification. The experimental tests and analysis results reveal that the appropriateness of the installation of certain traffic control devices can be statistically verified by TCDSM. The proposed approach provides a generic framework to assess traffic control device performance in school zones including experiment design, statistical formulation, data analysis, simulation model implementation, data interpretation, and recommendation development. Copyright © 2015 Elsevier Ltd. All rights reserved.

On-board processing for future satellite communications systems: Satellite-Routed FDMA

NASA Astrophysics Data System (ADS)

Berk, G.; Christopher, P. F.; Hoffman, M.; Jean, P. N.; Rotholz, E.; White, B. E.

1981-05-01

A frequency division multiple access (FDMA) 30/20 GHz satellite communications architecture without on-board baseband processing is investigated. Conceptual system designs are suggested for domestic traffic models totaling 4 Gb/s of customer premises service (CPS) traffic and 6 Gb/s of trunking traffic. Emphasis is given to the CPS portion of the system which includes thousands of earth terminals with digital traffic ranging from a single 64 kb/s voice channel to hundreds of channels of voice, data, and video with an aggregate data rate of 33 Mb/s. A unique regional design concept that effectively smooths the non-uniform traffic distribution and greatly simplifies the satellite design is employed. The satellite antenna system forms thirty-two 0.33 deg beam on both the uplinks and the downlinks in one design. In another design matched to a traffic model with more dispersed users, there are twenty-four 0.33 deg beams and twenty-one 0.7 deg beams. Detailed system design techniques show that a single satellite producing approximately 5 kW of dc power is capable of handling at least 75% of the postulated traffic. A detailed cost model of the ground segment and estimated system costs based on current information from manufacturers are presented.

On-board processing for future satellite communications systems: Satellite-Routed FDMA

NASA Technical Reports Server (NTRS)

Berk, G.; Christopher, P. F.; Hoffman, M.; Jean, P. N.; Rotholz, E.; White, B. E.

1981-01-01

A frequency division multiple access (FDMA) 30/20 GHz satellite communications architecture without on-board baseband processing is investigated. Conceptual system designs are suggested for domestic traffic models totaling 4 Gb/s of customer premises service (CPS) traffic and 6 Gb/s of trunking traffic. Emphasis is given to the CPS portion of the system which includes thousands of earth terminals with digital traffic ranging from a single 64 kb/s voice channel to hundreds of channels of voice, data, and video with an aggregate data rate of 33 Mb/s. A unique regional design concept that effectively smooths the non-uniform traffic distribution and greatly simplifies the satellite design is employed. The satellite antenna system forms thirty-two 0.33 deg beam on both the uplinks and the downlinks in one design. In another design matched to a traffic model with more dispersed users, there are twenty-four 0.33 deg beams and twenty-one 0.7 deg beams. Detailed system design techniques show that a single satellite producing approximately 5 kW of dc power is capable of handling at least 75% of the postulated traffic. A detailed cost model of the ground segment and estimated system costs based on current information from manufacturers are presented.

Towards Realistic Urban Traffic Experiments Using DFROUTER: Heuristic, Validation and Extensions

PubMed Central

2017-01-01

Traffic congestion is an important problem faced by Intelligent Transportation Systems (ITS), requiring models that allow predicting the impact of different solutions on urban traffic flow. Such an approach typically requires the use of simulations, which should be as realistic as possible. However, achieving high degrees of realism can be complex when the actual traffic patterns, defined through an Origin/Destination (O-D) matrix for the vehicles in a city, remain unknown. Thus, the main contribution of this paper is a heuristic for improving traffic congestion modeling. In particular, we propose a procedure that, starting from real induction loop measurements made available by traffic authorities, iteratively refines the output of DFROUTER, which is a module provided by the SUMO (Simulation of Urban MObility) tool. This way, it is able to generate an O-D matrix for traffic that resembles the real traffic distribution and that can be directly imported by SUMO. We apply our technique to the city of Valencia, and we then compare the obtained results against other existing traffic mobility data for the cities of Cologne (Germany) and Bologna (Italy), thereby validating our approach. We also use our technique to determine what degree of congestion is expectable if certain conditions cause additional traffic to circulate in the city, adopting both a uniform pattern and a hotspot-based pattern for traffic injection to demonstrate how to regulate the overall number of vehicles in the city. This study allows evaluating the impact of vehicle flow changes on the overall traffic congestion levels. PMID:29244762

Deterministic models for traffic jams

NASA Astrophysics Data System (ADS)

Nagel, Kai; Herrmann, Hans J.

1993-10-01

We study several deterministic one-dimensional traffic models. For integer positions and velocities we find the typical high and low density phases separated by a simple transition. If positions and velocities are continuous variables the model shows self-organized critically driven by the slowest car.

Road Network State Estimation Using Random Forest Ensemble Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hou, Yi; Edara, Praveen; Chang, Yohan

Network-scale travel time prediction not only enables traffic management centers (TMC) to proactively implement traffic management strategies, but also allows travelers make informed decisions about route choices between various origins and destinations. In this paper, a random forest estimator was proposed to predict travel time in a network. The estimator was trained using two years of historical travel time data for a case study network in St. Louis, Missouri. Both temporal and spatial effects were considered in the modeling process. The random forest models predicted travel times accurately during both congested and uncongested traffic conditions. The computational times for themore » models were low, thus useful for real-time traffic management and traveler information applications.« less

Soliton and kink jams in traffic flow with open boundaries.

PubMed

Muramatsu, M; Nagatani, T

1999-07-01

Soliton density wave is investigated numerically and analytically in the optimal velocity model (a car-following model) of a one-dimensional traffic flow with open boundaries. Soliton density wave is distinguished from the kink density wave. It is shown that the soliton density wave appears only at the threshold of occurrence of traffic jams. The Korteweg-de Vries (KdV) equation is derived from the optimal velocity model by the use of the nonlinear analysis. It is found that the traffic soliton appears only near the neutral stability line. The soliton solution is analytically obtained from the perturbed KdV equation. It is shown that the soliton solution obtained from the nonlinear analysis is consistent with that of the numerical simulation.

Effective environmental factors on geographical distribution of traffic accidents on pedestrians, downtown Tehran city.

PubMed

Moradi, Ali; Soori, Hamid; Kavousi, Amir; Eshghabadi, Farshid; Nematollahi, Shahrzad; Zeini, Salahdien

2017-01-01

In most countries, occurrence of traffic causalities is high in pedestrians. The aim of this study is to geographically analyze the traffic casualties in pedestrians in downtown Tehran city. The study population consisted of traffic injury accidents in pedestrians occurred during 2015 in Tehran city. Data were extracted from offices of traffic police and municipality. For analysis of environmental factors and site of accidents, ordinary least square regression models and geographically weighted regression were used. Fitness and performance of models were checked using the Akaike information criteria, Bayesian information criteria, deviance, and adjusted R 2 . Totally, 514 accidents were included in this study. Of them, site of accidents was arterial streets in 370 (71.9%) cases, collector streets in 133 cases (25.2%), and highways in 11 cases (2.1%). Geographical units of traffic accidents in pedestrians had statistically significant relationship with a number of bus stations, number of crossroads, and recreational areas. Distribution of injury traffic accidents in pedestrians is different in downtown Tehran city. Neighborhoods close to markets are considered as most dangerous neighborhoods for injury traffic accidents. Different environmental factors are involved in determining the distribution of these accidents. The health of pedestrians in Tehran city can be improved by proper traffic management, control of environmental factors, and educational programs.

Microscopic simulation model calibration and validation handbook.

DOT National Transportation Integrated Search

2006-01-01

Microscopic traffic simulation models are widely used in the transportation engineering field. Because of their cost-effectiveness, risk-free nature, and high-speed benefits, areas of use include transportation system design, traffic operations, and ...

Surrogate safety measures from traffic simulation models

DOT National Transportation Integrated Search

2003-01-01

This project investigates the potential for deriving surrogate measures of safety from existing microscopic traffic simulation models for intersections. The process of computing the measures in the simulation, extracting the required data, and summar...

Multi-level Bayesian safety analysis with unprocessed Automatic Vehicle Identification data for an urban expressway.

PubMed

Shi, Qi; Abdel-Aty, Mohamed; Yu, Rongjie

2016-03-01

In traffic safety studies, crash frequency modeling of total crashes is the cornerstone before proceeding to more detailed safety evaluation. The relationship between crash occurrence and factors such as traffic flow and roadway geometric characteristics has been extensively explored for a better understanding of crash mechanisms. In this study, a multi-level Bayesian framework has been developed in an effort to identify the crash contributing factors on an urban expressway in the Central Florida area. Two types of traffic data from the Automatic Vehicle Identification system, which are the processed data capped at speed limit and the unprocessed data retaining the original speed were incorporated in the analysis along with road geometric information. The model framework was proposed to account for the hierarchical data structure and the heterogeneity among the traffic and roadway geometric data. Multi-level and random parameters models were constructed and compared with the Negative Binomial model under the Bayesian inference framework. Results showed that the unprocessed traffic data was superior. Both multi-level models and random parameters models outperformed the Negative Binomial model and the models with random parameters achieved the best model fitting. The contributing factors identified imply that on the urban expressway lower speed and higher speed variation could significantly increase the crash likelihood. Other geometric factors were significant including auxiliary lanes and horizontal curvature. Copyright © 2015 Elsevier Ltd. All rights reserved.

Traffic Surveillance Data Processing in Urban Freeway Corridors Using Kalman Filter Techniques

DOT National Transportation Integrated Search

1978-11-01

Real-time surveillance of traffic conditions on urban freeway corridors using spatially discrete presence detectors is addressed. Using a finite-dimensional (macroscopic) fluid-analog model for freeway vehicular traffic flow, an extended Kalman filte...

A large-scale measurement, analysis and modelling of electromagnetic radiation levels in the vicinity of GSM/UMTS base stations in an urban area.

PubMed

Karadağ, Teoman; Yüceer, Mehmet; Abbasov, Teymuraz

2016-01-01

The present study analyses the electric field radiating from the GSM/UMTS base stations located in central Malatya, a densely populated urban area in Turkey. The authors have conducted both instant and continuous measurements of high-frequency electromagnetic fields throughout their research by using non-ionising radiation-monitoring networks. Over 15,000 instant and 13,000,000 continuous measurements were taken throughout the process. The authors have found that the normal electric field radiation can increase ∼25% during daytime, depending on mobile communication traffic. The authors' research work has also demonstrated the fact that the electric field intensity values can be modelled for each hour, day or week with the results obtained from continuous measurements. The authors have developed an estimation model based on these values, including mobile communication traffic (Erlang) values obtained from mobile phone base stations and the temperature and humidity values in the environment. The authors believe that their proposed artificial neural network model and multivariable least-squares regression analysis will help predict the electric field intensity in an environment in advance. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Measurements of particles in the 5-1000 nm range close to road level in an urban street canyon.

PubMed

Kumar, Prashant; Fennell, Paul; Britter, Rex

2008-02-15

A newly developed instrument, the 'fast response differential mobility spectrometer (DMS500)', was deployed to measure the particles in the 5-1000 nm range in a Cambridge (UK) street canyon. Measurements were taken for 7 weekdays (from 09:00 to 19:00 h) between 8 and 21 June 2006 at three heights close to the road level (i.e. 0.20 m, 1.0 m and 2.60 m). The main aims of the measurements were to investigate the dependence of particle number distributions (PNDs) and concentrations (PNCs) and their vertical variations on wind speed, wind direction, traffic volume, and to estimate the particle number flux (PNF) and the particle number emission factors (PNEF) for typical urban streets and driving conditions. Traffic was the main source of particles at the measurement site. Measured PNCs were inversely proportional to the reference wind speed and directly proportional to the traffic volume. During the periods of cross-canyon flow the PNCs were larger on the leeward side than the windward side of the street canyon showing a possible effect of the vortex circulation. The largest PNCs were unsurprisingly near to road level and the pollution sources. The PNCs measured at 0.20 m and 1.0 m were the same to within 0.5-12.5% indicating a well-mixed region and this was presumably due to the enhanced mixing from traffic produced turbulence. The PNCs at 2.60 m were lower by 10-40% than those at 0.20 m and 1.0 m, suggesting a possible concentration gradient in the upper part of the canyon. The PNFs were estimated using an idealised and an operational approach; they were directly proportional to the traffic volume confirming the traffic to be the main source of particles. The PNEF were estimated using an inverse modelling technique; the reported values were within a factor of 3 of those published in similar studies.

GENERAL: A modified weighted probabilistic cellular automaton traffic flow model

NASA Astrophysics Data System (ADS)

Zhuang, Qian; Jia, Bin; Li, Xin-Gang

2009-08-01

This paper modifies the weighted probabilistic cellular automaton model (Li X L, Kuang H, Song T, et al 2008 Chin. Phys. B 17 2366) which considered a diversity of traffic behaviors under real traffic situations induced by various driving characters and habits. In the new model, the effects of the velocity at the last time step and drivers' desire for acceleration are taken into account. The fundamental diagram, spatial-temporal diagram, and the time series of one-minute data are analyzed. The results show that this model reproduces synchronized flow. Finally, it simulates the on-ramp system with the proposed model. Some characteristics including the phase diagram are studied.

Synchronized flow in oversaturated city traffic.

PubMed

Kerner, Boris S; Klenov, Sergey L; Hermanns, Gerhard; Hemmerle, Peter; Rehborn, Hubert; Schreckenberg, Michael

2013-11-01

Based on numerical simulations with a stochastic three-phase traffic flow model, we reveal that moving queues (moving jams) in oversaturated city traffic dissolve at some distance upstream of the traffic signal while transforming into synchronized flow. It is found that, as in highway traffic [Kerner, Phys. Rev. E 85, 036110 (2012)], such a jam-absorption effect in city traffic is explained by a strong driver's speed adaptation: Time headways (space gaps) between vehicles increase upstream of a moving queue (moving jam), resulting in moving queue dissolution. It turns out that at given traffic signal parameters, the stronger the speed adaptation effect, the shorter the mean distance between the signal location and the road location at which moving queues dissolve fully and oversaturated traffic consists of synchronized flow only. A comparison of the synchronized flow in city traffic found in this Brief Report with synchronized flow in highway traffic is made.

Surveying traffic congestion based on the concept of community structure of complex networks

NASA Astrophysics Data System (ADS)

Ma, Lili; Zhang, Zhanli; Li, Meng

2016-07-01

In this paper, taking the traffic of Beijing city as an instance, we study city traffic states, especially traffic congestion, based on the concept of network community structure. Concretely, using the floating car data (FCD) information of vehicles gained from the intelligent transport system (ITS) of the city, we construct a new traffic network model which is with floating cars as network nodes and time-varying. It shows that this traffic network has Gaussian degree distributions at different time points. Furthermore, compared with free traffic situations, our simulations show that the traffic network generally has more obvious community structures with larger values of network fitness for congested traffic situations, and through the GPSspg web page, we show that all of our results are consistent with the reality. Then, it indicates that network community structure should be an available way for investigating city traffic congestion problems.

Traffic Sign Detection Based on Biologically Visual Mechanism

NASA Astrophysics Data System (ADS)

Hu, X.; Zhu, X.; Li, D.

2012-07-01

TSR (Traffic sign recognition) is an important problem in ITS (intelligent traffic system), which is being paid more and more attention for realizing drivers assisting system and unmanned vehicle etc. TSR consists of two steps: detection and recognition, and this paper describe a new traffic sign detection method. The design principle of the traffic sign is comply with the visual attention mechanism of human, so we propose a method using visual attention mechanism to detect traffic sign ,which is reasonable. In our method, the whole scene will firstly be analyzed by visual attention model to acquire the area where traffic signs might be placed. And then, these candidate areas will be analyzed according to the shape characteristics of the traffic sign to detect traffic signs. In traffic sign detection experiments, the result shows the proposed method is effectively and robust than other existing saliency detection method.

Synchronized flow in oversaturated city traffic

NASA Astrophysics Data System (ADS)

Kerner, Boris S.; Klenov, Sergey L.; Hermanns, Gerhard; Hemmerle, Peter; Rehborn, Hubert; Schreckenberg, Michael

2013-11-01

Based on numerical simulations with a stochastic three-phase traffic flow model, we reveal that moving queues (moving jams) in oversaturated city traffic dissolve at some distance upstream of the traffic signal while transforming into synchronized flow. It is found that, as in highway traffic [Kerner, Phys. Rev. EPLEEE81539-375510.1103/PhysRevE.85.036110 85, 036110 (2012)], such a jam-absorption effect in city traffic is explained by a strong driver's speed adaptation: Time headways (space gaps) between vehicles increase upstream of a moving queue (moving jam), resulting in moving queue dissolution. It turns out that at given traffic signal parameters, the stronger the speed adaptation effect, the shorter the mean distance between the signal location and the road location at which moving queues dissolve fully and oversaturated traffic consists of synchronized flow only. A comparison of the synchronized flow in city traffic found in this Brief Report with synchronized flow in highway traffic is made.

Network traffic behaviour near phase transition point

NASA Astrophysics Data System (ADS)

Lawniczak, A. T.; Tang, X.

2006-03-01

We explore packet traffic dynamics in a data network model near phase transition point from free flow to congestion. The model of data network is an abstraction of the Network Layer of the OSI (Open Systems Interconnect) Reference Model of packet switching networks. The Network Layer is responsible for routing packets across the network from their sources to their destinations and for control of congestion in data networks. Using the model we investigate spatio-temporal packets traffic dynamics near the phase transition point for various network connection topologies, and static and adaptive routing algorithms. We present selected simulation results and analyze them.

Understanding re-distribution of road deposited particle-bound pollutants using a Bayesian Network (BN) approach.

PubMed

Liu, An; Wijesiri, Buddhi; Hong, Nian; Zhu, Panfeng; Egodawatta, Prasanna; Goonetilleke, Ashantha

2018-05-08

Road deposited pollutants (build-up) are continuously re-distributed by external factors such as traffic and wind turbulence, influencing stormwater runoff quality. However, current stormwater quality modelling approaches do not account for the re-distribution of pollutants. This undermines the accuracy of stormwater quality predictions, constraining the design of effective stormwater treatment measures. This study, using over 1000 data points, developed a Bayesian Network modelling approach to investigate the re-distribution of pollutant build-up on urban road surfaces. BTEX, which are a group of highly toxic pollutants, was the case study pollutants. Build-up sampling was undertaken in Shenzhen, China, using a dry and wet vacuuming method. The research outcomes confirmed that the vehicle type and particle size significantly influence the re-distribution of particle-bound BTEX. Compared to heavy-duty traffic in commercial areas, light-duty traffic dominates the re-distribution of particles of all size ranges. In industrial areas, heavy-duty traffic re-distributes particles >75 μm, and light-duty traffic re-distributes particles <75 μm. In residential areas, light-duty traffic re-distributes particles >300 μm and <75 μm and heavy-duty traffic re-distributes particles in the 300-150 μm range. The study results provide important insights to improve stormwater quality modelling and the interpretation of modelling outcomes, contributing to safeguard the urban water environment. Copyright © 2018 Elsevier B.V. All rights reserved.

Modeling mechanical restriction differences between car and heavy truck in two-lane cellular automata traffic flow model

NASA Astrophysics Data System (ADS)

Li, Xin; Li, Xingang; Xiao, Yao; Jia, Bin

2016-06-01

Real traffic is heterogeneous with car and truck. Due to mechanical restrictions, the car and the truck have different limited deceleration capabilities, which are important factors in safety driving. This paper extends the single lane safety driving (SD) model with limited deceleration capability to two-lane SD model, in which car-truck heterogeneous traffic is considered. A car has a larger limited deceleration capability while a heavy truck has a smaller limited deceleration capability as a result of loaded goods. Then the safety driving conditions are different as the types of the following and the leading vehicles vary. In order to eliminate the well-known plug in heterogeneous two-lane traffic, it is assumed that heavy truck has active deceleration behavior when the heavy truck perceives the forming plug. The lane-changing decisions are also determined by the safety driving conditions. The fundamental diagram, spatiotemporal diagram, and lane-changing frequency were investigated to show the effect of mechanical restriction on heterogeneous traffic flow. It was shown that there would be still three traffic phases in heterogeneous traffic condition; the active deceleration of the heavy truck could well eliminate the plug; the lane-changing frequency was low in synchronized flow; the flow and velocity would decrease as the proportion of heavy truck grows or the limited deceleration capability of heavy truck drops; and the flow could be improved with lane control measures.

Evaluation of Traffic Density Parameters as an Indicator of Vehicle Emission-Related Near-Road Air Pollution: A Case Study with NEXUS Measurement Data on Black Carbon

EPA Science Inventory

An important factor in evaluating health risk of near-road air pollution is to accurately estimate the traffic-related vehicle emission of air pollutants. Inclusion of traffic parameters such as road length/area, distance to roads, and traffic volume/intensity into models such as...

Game theory model of traffic participants within amber time at signalized intersection.

PubMed

Qi, Weiwei; Wen, Huiying; Fu, Chuanyun; Song, Mo

2014-01-01

The traffic light scheme is composed of red, green, and amber lights, and it has been defined clearly for the traffic access of red and green lights; however, the definition of that for the amber light is indistinct, which leads to the appearance of uncertainty factors and serious traffic conflicts during the amber light. At present, the traffic administrations are faced with the decision of whether to forbid passing or not during the amber light in the cities of China. On one hand, it will go against the purpose of setting amber lights if forbidding passing; on the other hand, it may lead to a mess of traffic flow running if not. And meanwhile the drivers are faced with the decision of passing the intersection or stopping during the amber light as well. So the decision-making behavior of traffic administrations and drivers can be converted into a double game model. And through quantification of their earnings in different choice conditions, the optimum decision-making plan under specific conditions could be solved via the Nash equilibrium solution concept. Thus the results will provide a basis for the formulation of the traffic management strategy.

Game Theory Model of Traffic Participants within Amber Time at Signalized Intersection

PubMed Central

Qi, Weiwei; Wen, Huiying; Fu, Chuanyun; Song, Mo

2014-01-01

The traffic light scheme is composed of red, green, and amber lights, and it has been defined clearly for the traffic access of red and green lights; however, the definition of that for the amber light is indistinct, which leads to the appearance of uncertainty factors and serious traffic conflicts during the amber light. At present, the traffic administrations are faced with the decision of whether to forbid passing or not during the amber light in the cities of China. On one hand, it will go against the purpose of setting amber lights if forbidding passing; on the other hand, it may lead to a mess of traffic flow running if not. And meanwhile the drivers are faced with the decision of passing the intersection or stopping during the amber light as well. So the decision-making behavior of traffic administrations and drivers can be converted into a double game model. And through quantification of their earnings in different choice conditions, the optimum decision-making plan under specific conditions could be solved via the Nash equilibrium solution concept. Thus the results will provide a basis for the formulation of the traffic management strategy. PMID:25580108

The integrity of the plant Golgi apparatus depends on cell growth-controlled activity of GNL1.

PubMed

Du, Wenyan; Tamura, Kentaro; Stefano, Giovanni; Brandizzi, Federica

2013-05-01

Membrane traffic and organelle integrity in the plant secretory pathway depend on ARF-GTPases, which are activated by guanine-nucleotide exchange factors (ARF-GEFs). While maintenance of conserved roles, evolution of unique functions as well as tissue-specific roles have been shown for a handful of plant ARF-GEFs, a fundamental yet unanswered question concerns the extent to which their function overlaps during cell growth. To address this, we have characterized pao, a novel allele of GNOM-like 1 (GNL1), a brefeldin A (BFA)-insensitive ARF-GEF, isolated through a confocal microscopy-based forward genetics screen of the Golgi in Arabidopsis thaliana. Specifically, we have analyzed the dependence of the integrity of trafficking routes and secretory organelles on GNL1 availability during expansion stages of cotyledon epidermal cells, an exquisite model system for vegetative cell growth analyses in intact tissues. We show that Golgi traffic is influenced largely by GNL1 availability at early stages of cotyledon cell expansion but by BFA-sensitive GEFs when cell growth terminates. These data reveal an unanticipated level of complexity in the biology of GNL1 by showing that its cellular roles are correlated with cell growth. These results also indicate that the cell growth stage is an important element weighting into functional analyses of the cellular roles of ARF-GEFs.

Stochastic Analysis and Applied Probability(3.3.1): Topics in the Theory and Applications of Stochastic Analysis

DTIC Science & Technology

2015-08-13

is due to Reiman [36] who considered the case where the arrivals and services are mutually independent renewal processes with square integrable summands...to a reflected diffusion process with drift and diffusion coefficients that depend on the state of the process. In models considered in works of Reiman ...the infinity Laplacian. Jour. AMS, to appear [36] M. I. Reiman . Open queueing networks in heavy traffic. Mathematics of Operations Research, 9(3): 441

An improved car-following model with two preceding cars' average speed

NASA Astrophysics Data System (ADS)

Yu, Shao-Wei; Shi, Zhong-Ke

2015-01-01

To better describe cooperative car-following behaviors under intelligent transportation circumstances and increase roadway traffic mobility, the data of three successive following cars at a signalized intersection of Jinan in China were obtained and employed to explore the linkage between two preceding cars' average speed and car-following behaviors. The results indicate that two preceding cars' average velocity has significant effects on the following car's motion. Then an improved car-following model considering two preceding cars' average velocity was proposed and calibrated based on full velocity difference model and some numerical simulations were carried out to study how two preceding cars' average speed affected the starting process and the traffic flow evolution process with an initial small disturbance, the results indicate that the improved car-following model can qualitatively describe the impacts of two preceding cars' average velocity on traffic flow and that taking two preceding cars' average velocity into account in designing the control strategy for the cooperative adaptive cruise control system can improve the stability of traffic flow, suppress the appearance of traffic jams and increase the capacity of signalized intersections.

Traffic effects on bird counts on North American Breeding Bird Survey routes

USGS Publications Warehouse

Griffith, Emily H.; Sauer, John R.; Royle, J. Andrew

2010-01-01

The North American Breeding Bird Survey (BBS) is an annual roadside survey used to estimate population change in >420 species of birds that breed in North America. Roadside sampling has been criticized, in part because traffic noise can interfere with bird counts. Since 1997, data have been collected on the numbers of vehicles that pass during counts at each stop. We assessed the effect of traffic by modeling total vehicles as a covariate of counts in hierarchical Poisson regression models used to estimate population change. We selected species for analysis that represent birds detected at low and high abundance and birds with songs of low and high frequencies. Increases in vehicle counts were associated with decreases in bird counts in most of the species examined. The size and direction of these effects remained relatively constant between two alternative models that we analyzed. Although this analysis indicated only a small effect of incorporating traffic effects when modeling roadside counts of birds, we suggest that continued evaluation of changes in traffic at BBS stops should be a component of future BBS analyses.

Congested traffic states in empirical observations and microscopic simulations

NASA Astrophysics Data System (ADS)

Treiber, Martin; Hennecke, Ansgar; Helbing, Dirk

2000-08-01

We present data from several German freeways showing different kinds of congested traffic forming near road inhomogeneities, specifically lane closings, intersections, or uphill gradients. The states are localized or extended, homogeneous or oscillating. Combined states are observed as well, like the coexistence of moving localized clusters and clusters pinned at road inhomogeneities, or regions of oscillating congested traffic upstream of nearly homogeneous congested traffic. The experimental findings are consistent with a recently proposed theoretical phase diagram for traffic near on-ramps [D. Helbing, A. Hennecke, and M. Treiber, Phys. Rev. Lett. 82, 4360 (1999)]. We simulate these situations with a continuous microscopic single-lane model, the ``intelligent driver model,'' using empirical boundary conditions. All observations, including the coexistence of states, are qualitatively reproduced by describing inhomogeneities with local variations of one model parameter. We show that the results of the microscopic model can be understood by formulating the theoretical phase diagram for bottlenecks in a more general way. In particular, a local drop of the road capacity induced by parameter variations has essentially the same effect as an on-ramp.

Using Virtual Reality to Assess Ethical Decisions in Road Traffic Scenarios: Applicability of Value-of-Life-Based Models and Influences of Time Pressure.

PubMed

Sütfeld, Leon R; Gast, Richard; König, Peter; Pipa, Gordon

2017-01-01

Self-driving cars are posing a new challenge to our ethics. By using algorithms to make decisions in situations where harming humans is possible, probable, or even unavoidable, a self-driving car's ethical behavior comes pre-defined. Ad hoc decisions are made in milliseconds, but can be based on extensive research and debates. The same algorithms are also likely to be used in millions of cars at a time, increasing the impact of any inherent biases, and increasing the importance of getting it right. Previous research has shown that moral judgment and behavior are highly context-dependent, and comprehensive and nuanced models of the underlying cognitive processes are out of reach to date. Models of ethics for self-driving cars should thus aim to match human decisions made in the same context. We employed immersive virtual reality to assess ethical behavior in simulated road traffic scenarios, and used the collected data to train and evaluate a range of decision models. In the study, participants controlled a virtual car and had to choose which of two given obstacles they would sacrifice in order to spare the other. We randomly sampled obstacles from a variety of inanimate objects, animals and humans. Our model comparison shows that simple models based on one-dimensional value-of-life scales are suited to describe human ethical behavior in these situations. Furthermore, we examined the influence of severe time pressure on the decision-making process. We found that it decreases consistency in the decision patterns, thus providing an argument for algorithmic decision-making in road traffic. This study demonstrates the suitability of virtual reality for the assessment of ethical behavior in humans, delivering consistent results across subjects, while closely matching the experimental settings to the real world scenarios in question.

Using Virtual Reality to Assess Ethical Decisions in Road Traffic Scenarios: Applicability of Value-of-Life-Based Models and Influences of Time Pressure

PubMed Central

Sütfeld, Leon R.; Gast, Richard; König, Peter; Pipa, Gordon

2017-01-01

Self-driving cars are posing a new challenge to our ethics. By using algorithms to make decisions in situations where harming humans is possible, probable, or even unavoidable, a self-driving car's ethical behavior comes pre-defined. Ad hoc decisions are made in milliseconds, but can be based on extensive research and debates. The same algorithms are also likely to be used in millions of cars at a time, increasing the impact of any inherent biases, and increasing the importance of getting it right. Previous research has shown that moral judgment and behavior are highly context-dependent, and comprehensive and nuanced models of the underlying cognitive processes are out of reach to date. Models of ethics for self-driving cars should thus aim to match human decisions made in the same context. We employed immersive virtual reality to assess ethical behavior in simulated road traffic scenarios, and used the collected data to train and evaluate a range of decision models. In the study, participants controlled a virtual car and had to choose which of two given obstacles they would sacrifice in order to spare the other. We randomly sampled obstacles from a variety of inanimate objects, animals and humans. Our model comparison shows that simple models based on one-dimensional value-of-life scales are suited to describe human ethical behavior in these situations. Furthermore, we examined the influence of severe time pressure on the decision-making process. We found that it decreases consistency in the decision patterns, thus providing an argument for algorithmic decision-making in road traffic. This study demonstrates the suitability of virtual reality for the assessment of ethical behavior in humans, delivering consistent results across subjects, while closely matching the experimental settings to the real world scenarios in question. PMID:28725188

Spatial regression analysis of traffic crashes in Seoul.

PubMed

Rhee, Kyoung-Ah; Kim, Joon-Ki; Lee, Young-ihn; Ulfarsson, Gudmundur F

2016-06-01

Traffic crashes can be spatially correlated events and the analysis of the distribution of traffic crash frequency requires evaluation of parameters that reflect spatial properties and correlation. Typically this spatial aspect of crash data is not used in everyday practice by planning agencies and this contributes to a gap between research and practice. A database of traffic crashes in Seoul, Korea, in 2010 was developed at the traffic analysis zone (TAZ) level with a number of GIS developed spatial variables. Practical spatial models using available software were estimated. The spatial error model was determined to be better than the spatial lag model and an ordinary least squares baseline regression. A geographically weighted regression model provided useful insights about localization of effects. The results found that an increased length of roads with speed limit below 30 km/h and a higher ratio of residents below age of 15 were correlated with lower traffic crash frequency, while a higher ratio of residents who moved to the TAZ, more vehicle-kilometers traveled, and a greater number of access points with speed limit difference between side roads and mainline above 30 km/h all increased the number of traffic crashes. This suggests, for example, that better control or design for merging lower speed roads with higher speed roads is important. A key result is that the length of bus-only center lanes had the largest effect on increasing traffic crashes. This is important as bus-only center lanes with bus stop islands have been increasingly used to improve transit times. Hence the potential negative safety impacts of such systems need to be studied further and mitigated through improved design of pedestrian access to center bus stop islands. Copyright © 2016 Elsevier Ltd. All rights reserved.

Combined effects of road traffic noise and ambient air pollution in relation to risk for stroke?

PubMed

Sørensen, Mette; Lühdorf, Pernille; Ketzel, Matthias; Andersen, Zorana J; Tjønneland, Anne; Overvad, Kim; Raaschou-Nielsen, Ole

2014-08-01

Exposure to road traffic noise and air pollution have both been associated with risk for stroke. The few studies including both exposures show inconsistent results. We aimed to investigate potential mutual confounding and combined effects between road traffic noise and air pollution in association with risk for stroke. In a population-based cohort of 57,053 people aged 50-64 years at enrollment, we identified 1999 incident stroke cases in national registries, followed by validation through medical records. Mean follow-up time was 11.2 years. Present and historical residential addresses from 1987 to 2009 were identified in national registers and road traffic noise and air pollution were modeled for all addresses. Analyses were done using Cox regression. A higher mean annual exposure at time of diagnosis of 10 µg/m(3) nitrogen dioxide (NO2) and 10 dB road traffic noise at the residential address was associated with ischemic stroke with incidence rate ratios (IRR) of 1.11 (95% CI: 1.03, 1.20) and 1.16 (95% CI: 1.07, 1.24), respectively, in single exposure models. In two-exposure models road traffic noise (IRR: 1.15) and not NO2 (IRR: 1.02) was associated with ischemic stroke. The strongest association was found for combination of high noise and high NO2 (IRR=1.28; 95% CI=1.09-1.52). Fatal stroke was positively associated with air pollution and not with traffic noise. In conclusion, in mutually adjusted models road traffic noise and not air pollution was associated ischemic stroke, while only air pollution affected risk for fatal strokes. There were indications of combined effects. Copyright © 2014 Elsevier Inc. All rights reserved.

TEST OF A THEORETICAL COMMUTER EXPOSURE MODEL TO VEHICLE EXHAUST IN TRAFFIC

EPA Science Inventory

A theoretical model of commuter exposure is presented as a box or cell model with the automobile passenger compartment representing the microenvironment exposed to CO concentrations resulting from vehicle exhaust leaks and emissions from traffic. Equations which describe this sit...

A data storage and retrieval model for Louisiana traffic operations data : technical summary.

DOT National Transportation Integrated Search

1996-08-01

The overall goal of this research study was to develop a prototype computer-based indexing model for traffic operation data in DOTD. The methodology included: 1) extraction of state road network, 2) development of geographic reference model, 3) engin...

Addendum to validation of FHWA's Traffic Noise Model (TNM) : phase 1

DOT National Transportation Integrated Search

2004-07-01

(FHWA) is conducting a multiple-phase study to assess the accuracy and make recommendations on the use of FHWAs Traffic Noise Model (TNM). The TNM Validation Study involves highway noise data collection and TNM modeling for the purpose of data com...

An empirical investigation of spatial differentiation and price floor regulations in retail markets for gasoline

NASA Astrophysics Data System (ADS)

Houde, Jean-Francois

In the first essay of this dissertation, I study an empirical model of spatial competition. The main feature of my approach is to formally specify commuting paths as the "locations" of consumers in a Hotelling-type model of spatial competition. The main consequence of this location assumption is that the substitution patterns between stations depend in an intuitive way on the structure of the road network and the direction of traffic flows. The demand-side of the model is estimated by combining a model of traffic allocation with econometric techniques used to estimate models of demand for differentiated products (Berry, Levinsohn and Pakes (1995)). The estimated parameters are then used to evaluate the importance of commuting patterns in explaining the distribution of gasoline sales, and compare the economic predictions of the model with the standard home-location model. In the second and third essays, I examine empirically the effect of a price floor regulation on the dynamic and static equilibrium outcomes of the gasoline retail industry. In particular, in the second essay I study empirically the dynamic entry and exit decisions of gasoline stations, and measure the impact of a price floor on the continuation values of staying in the industry. In the third essay, I develop and estimate a static model of quantity competition subject to a price floor regulation. Both models are estimated using a rich panel dataset on the Quebec gasoline retail market before and after the implementation of a price floor regulation.

Statistical modeling of the Internet traffic dynamics: To which extent do we need long-term correlations?

NASA Astrophysics Data System (ADS)

Markelov, Oleg; Nguyen Duc, Viet; Bogachev, Mikhail

2017-11-01

Recently we have suggested a universal superstatistical model of user access patterns and aggregated network traffic. The model takes into account the irregular character of end user access patterns on the web via the non-exponential distributions of the local access rates, but neglects the long-term correlations between these rates. While the model is accurate for quasi-stationary traffic records, its performance under highly variable and especially non-stationary access dynamics remains questionable. In this paper, using an example of the traffic patterns from a highly loaded network cluster hosting the website of the 1998 FIFA World Cup, we suggest a generalization of the previously suggested superstatistical model by introducing long-term correlations between access rates. Using queueing system simulations, we show explicitly that this generalization is essential for modeling network nodes with highly non-stationary access patterns, where neglecting long-term correlations leads to the underestimation of the empirical average sojourn time by several decades under high throughput utilization.

Prediction of road traffic death rate using neural networks optimised by genetic algorithm.

PubMed

Jafari, Seyed Ali; Jahandideh, Sepideh; Jahandideh, Mina; Asadabadi, Ebrahim Barzegari

2015-01-01

Road traffic injuries (RTIs) are realised as a main cause of public health problems at global, regional and national levels. Therefore, prediction of road traffic death rate will be helpful in its management. Based on this fact, we used an artificial neural network model optimised through Genetic algorithm to predict mortality. In this study, a five-fold cross-validation procedure on a data set containing total of 178 countries was used to verify the performance of models. The best-fit model was selected according to the root mean square errors (RMSE). Genetic algorithm, as a powerful model which has not been introduced in prediction of mortality to this extent in previous studies, showed high performance. The lowest RMSE obtained was 0.0808. Such satisfactory results could be attributed to the use of Genetic algorithm as a powerful optimiser which selects the best input feature set to be fed into the neural networks. Seven factors have been known as the most effective factors on the road traffic mortality rate by high accuracy. The gained results displayed that our model is very promising and may play a useful role in developing a better method for assessing the influence of road traffic mortality risk factors.

Attentional models of multitask pilot performance using advanced display technology.

PubMed

Wickens, Christopher D; Goh, Juliana; Helleberg, John; Horrey, William J; Talleur, Donald A

2003-01-01

In the first part of the reported research, 12 instrument-rated pilots flew a high-fidelity simulation, in which air traffic control presentation of auditory (voice) information regarding traffic and flight parameters was compared with advanced display technology presentation of equivalent information regarding traffic (cockpit display of traffic information) and flight parameters (data link display). Redundant combinations were also examined while pilots flew the aircraft simulation, monitored for outside traffic, and read back communications messages. The data suggested a modest cost for visual presentation over auditory presentation, a cost mediated by head-down visual scanning, and no benefit for redundant presentation. The effects in Part 1 were modeled by multiple-resource and preemption models of divided attention. In the second part of the research, visual scanning in all conditions was fit by an expected value model of selective attention derived from a previous experiment. This model accounted for 94% of the variance in the scanning data and 90% of the variance in a second validation experiment. Actual or potential applications of this research include guidance on choosing the appropriate modality for presenting in-cockpit information and understanding task strategies induced by introducing new aviation technology.