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.

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.

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.

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.

Wave dynamics in an extended macroscopic traffic flow model with periodic boundaries

NASA Astrophysics Data System (ADS)

Wang, Yu-Qing; Chu, Xing-Jian; Zhou, Chao-Fan; Yan, Bo-Wen; Jia, Bin; Fang, Chen-Hao

2018-06-01

Motivated by the previous traffic flow model considering the real-time traffic state, a modified macroscopic traffic flow model is established. The periodic boundary condition is applied to the car-following model. Besides, the traffic state factor R is defined in order to correct the real traffic conditions in a more reasonable way. It is a key step that we introduce the relaxation time as a density-dependent function and provide corresponding evolvement of traffic flow. Three different typical initial densities, namely the high density, the medium one and the low one, are intensively investigated. It can be found that the hysteresis loop exists in the proposed periodic-boundary system. Furthermore, the linear and nonlinear stability analyses are performed in order to test the robustness of the system.

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,...

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.

Evidence of Long Range Dependence and Self-similarity in Urban Traffic Systems

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

Thakur, Gautam S; Helmy, Ahmed; Hui, Pan

2015-01-01

Transportation simulation technologies should accurately model traffic demand, distribution, and assignment parame- ters for urban environment simulation. These three param- eters significantly impact transportation engineering bench- mark process, are also critical in realizing realistic traffic modeling situations. In this paper, we model and charac- terize traffic density distribution of thousands of locations around the world. The traffic densities are generated from millions of images collected over several years and processed using computer vision techniques. The resulting traffic den- sity distribution time series are then analyzed. It is found using the goodness-of-fit test that the traffic density dis- tributions follows heavy-tailmore » models such as Log-gamma, Log-logistic, and Weibull in over 90% of analyzed locations. Moreover, a heavy-tail gives rise to long-range dependence and self-similarity, which we studied by estimating the Hurst exponent (H). Our analysis based on seven different Hurst estimators strongly indicate that the traffic distribution pat- terns are stochastically self-similar (0.5 H 1.0). We believe this is an important finding that will influence the design and development of the next generation traffic simu- lation techniques and also aid in accurately modeling traffic engineering of urban systems. In addition, it shall provide a much needed input for the development of smart cities.« less

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.

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.

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.

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)

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.

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

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.

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.

Impacts of moving bottlenecks on traffic flow

NASA Astrophysics Data System (ADS)

Ou, Hui; Tang, Tie-Qiao

2018-06-01

Bottleneck (especially the moving bottleneck) widely exists in the urban traffic system. However, little effort has been made to study the impacts of the moving bottleneck on traffic flow (especially the evolution and propagation of traffic flow). In this article, we introduce the speed of a moving bottleneck into a traffic flow model, then propose an extended macro traffic flow with a moving bottleneck, and finally use the proposed model to study the effects of a moving bottleneck on the evolution and propagation of traffic flow under uniform flow and a small perturbation. The numerical results indicate that the moving bottleneck has prominent influences on the evolution of traffic flow under the two typical traffic situations and that the impacts are dependent on the initial density.

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.

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

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...

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.

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.

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.

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

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...

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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...

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

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

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

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.

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.

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.

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%.

The Influence of Individual Driver Characteristics on Congestion Formation

NASA Astrophysics Data System (ADS)

Wang, Lanjun; Zhang, Hao; Meng, Huadong; Wang, Xiqin

Previous works have pointed out that one of the reasons for the formation of traffic congestion is instability in traffic flow. In this study, we investigate theoretically how the characteristics of individual drivers influence the instability of traffic flow. The discussions are based on the optimal velocity model, which has three parameters related to individual driver characteristics. We specify the mappings between the model parameters and driver characteristics in this study. With linear stability analysis, we obtain a condition for when instability occurs and a constraint about how the model parameters influence the unstable traffic flow. Meanwhile, we also determine how the region of unstable flow densities depends on these parameters. Additionally, the Langevin approach theoretically validates that under the constraint, the macroscopic characteristics of the unstable traffic flow becomes a mixture of free flows and congestions. All of these results imply that both overly aggressive and overly conservative drivers are capable of triggering traffic congestion.

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 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.

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.

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

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.

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.

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.

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.

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.

A spring-mass-damper system dynamics-based driver-vehicle integrated model for representing heterogeneous traffic

NASA Astrophysics Data System (ADS)

Munigety, Caleb Ronald

2018-04-01

The traditional traffic microscopic simulation models consider driver and vehicle as a single unit to represent the movements of drivers in a traffic stream. Due to this very fact, the traditional car-following models have the driver behavior related parameters, but ignore the vehicle related aspects. This approach is appropriate for homogeneous traffic conditions where car is the major vehicle type. However, in heterogeneous traffic conditions where multiple vehicle types are present, it becomes important to incorporate the vehicle related parameters exclusively to account for the varying dynamic and static characteristics. Thus, this paper presents a driver-vehicle integrated model hinged on the principles involved in physics-based spring-mass-damper mechanical system. While the spring constant represents the driver’s aggressiveness, the damping constant and the mass component take care of the stability and size/weight related aspects, respectively. The proposed model when tested, behaved pragmatically in representing the vehicle-type dependent longitudinal movements of vehicles.

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.

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

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.

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

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.

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 ...

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.

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.

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.

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.

Spontaneous density fluctuations in granular flow and traffic

NASA Astrophysics Data System (ADS)

Herrmann, Hans J.

It is known that spontaneous density waves appear in granular material flowing through pipes or hoppers. A similar phenomenon is known from traffic jams on highways. Using numerical simulations we show that several types of waves exist and find that the density fluctuations follow a power law spectrum. We also investigate one-dimensional traffic models. If positions and velocities are continuous variables the model shows self-organized criticality driven by the slowest car. Lattice gas and lattice Boltzmann models reproduce the experimentally observed effects. Density waves are spontaneously generated when the viscosity has a non-linear dependence on density or shear rate as it is the case in traffic or granular flow.

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.

Nonlinear density wave investigation for an extended car-following model considering driver’s memory and jerk

NASA Astrophysics Data System (ADS)

Jin, Zhizhan; Li, Zhipeng; Cheng, Rongjun; Ge, Hongxia

2018-01-01

Based on the two velocity difference model (TVDM), an extended car-following model is developed to investigate the effect of driver’s memory and jerk on traffic flow in this paper. By using linear stability analysis, the stability conditions are derived. And through nonlinear analysis, the time-dependent Ginzburg-Landau (TDGL) equation and the modified Korteweg-de Vries (mKdV) equation are obtained, respectively. The mKdV equation is constructed to describe the traffic behavior near the critical point. The evolution of traffic congestion and the corresponding energy consumption are discussed. Numerical simulations show that the improved model is found not only to enhance the stability of traffic flow, but also to depress the energy consumption, which are consistent with the theoretical analysis.

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.

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.

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.

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

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.

General Dynamics of Topology and Traffic on Weighted Technological Networks

NASA Astrophysics Data System (ADS)

Wang, Wen-Xu; Wang, Bing-Hong; Hu, Bo; Yan, Gang; Ou, Qing

2005-05-01

For most technical networks, the interplay of dynamics, traffic, and topology is assumed crucial to their evolution. In this Letter, we propose a traffic-driven evolution model of weighted technological networks. By introducing a general strength-coupling mechanism under which the traffic and topology mutually interact, the model gives power-law distributions of degree, weight, and strength, as confirmed in many real networks. Particularly, depending on a parameter W that controls the total weight growth of the system, the nontrivial clustering coefficient C, degree assortativity coefficient r, and degree-strength correlation are all consistent with empirical evidence.

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 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...

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

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.

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...

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.

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.

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.

Traffic Safety for Special Children

ERIC Educational Resources Information Center

Wilson, Val; MacKenzie, R. A.

1974-01-01

In a 6 weeks' unit on traffic education using flannel graphs, filmstrips and models, 12 special class students (IQ 55-82) ages 7- to 11-years-old learned six basic skills including crossing a road, obeying traffic lights and walking on country roads. (CL)

Dynamic traffic assignment based trailblazing guide signing for major traffic generator.

DOT National Transportation Integrated Search

2009-11-01

The placement of guide signs and the display of dynamic massage signs greatly affect drivers : understanding of the network and therefore their route choices. Most existing dynamic traffic assignment : models assume that drivers heading to a Major...

Use of mobile data for weather-responsive traffic management models.

DOT National Transportation Integrated Search

2012-10-01

The evolution of telecommunications and wireless technologies has brought in new sources of traffic data (particularly mobile data generated by vehicle probes), which could offer a breakthrough in the quality and extent of traffic data. This study re...

FHWA Study Tour For European Traffic Monitoring Programs and Technologies

DOT National Transportation Integrated Search

1998-07-01

In March 1998, the Federal Highway Administration (FHWA) released the FHWA Traffic Noise Model [FHWA TNM (registered trademark)], Version 1.0, a state-of-the-art computer program for highway traffic noise prediction and analysis. Comparisons have sho...

Traffic dispersion through a series of signals with irregular split

NASA Astrophysics Data System (ADS)

Nagatani, Takashi

2016-01-01

We study the traffic behavior of a group of vehicles moving through a sequence of signals with irregular splits on a roadway. We present the stochastic model of vehicular traffic controlled by signals. The dynamic behavior of vehicular traffic is clarified by analyzing traffic pattern and travel time numerically. The group of vehicles breaks up more and more by the irregularity of signal's split. The traffic dispersion is induced by the irregular split. We show that the traffic dispersion depends highly on the cycle time and the strength of split's irregularity. Also, we study the traffic behavior through the series of signals at the green-wave strategy. The dependence of the travel time on offset time is derived for various values of cycle time. The region map of the traffic dispersion is shown in (cycle time, offset time)-space.

Carbon emissions tax policy of urban road traffic and its application in Panjin, China

PubMed Central

Yang, Longhai; Fang, Lin

2018-01-01

How to effectively solve traffic congestion and transportation pollution in urban development is a main research emphasis for transportation management agencies. A carbon emissions tax can affect travelers’ generalized costs and will lead to changes in passenger demand, mode choice and traffic flow equilibrium in road networks, which are of significance in green travel and low-carbon transportation management. This paper first established a mesoscopic model to calculate the carbon emissions tax and determined the value of this charge in China, which was based on road traffic flow, vehicle speed, and carbon emissions. Referring to existing research results to calibrate the value of time, this paper modified the traveler’s generalized cost function, including the carbon emissions tax, fuel surcharge and travel time cost, which can be used in the travel impedance model with the consideration of the carbon emissions tax. Then, a method for analyzing urban road network traffic flow distribution was put forward, and a joint traffic distribution model was established, which considered the relationship between private cars and taxis. Finally, this paper took the city of Panjin as an example to analyze the road traffic carbon emissions tax’s impact. The results illustrated that the carbon emissions tax has a positive effect on road network flow equilibrium and carbon emission reduction. This paper will have good reference value and practical significance for the calculation and implementation of urban traffic carbon emissions taxes in China. PMID:29738580

Carbon emissions tax policy of urban road traffic and its application in Panjin, China.

PubMed

Yang, Longhai; Hu, Xiaowei; Fang, Lin

2018-01-01

How to effectively solve traffic congestion and transportation pollution in urban development is a main research emphasis for transportation management agencies. A carbon emissions tax can affect travelers' generalized costs and will lead to changes in passenger demand, mode choice and traffic flow equilibrium in road networks, which are of significance in green travel and low-carbon transportation management. This paper first established a mesoscopic model to calculate the carbon emissions tax and determined the value of this charge in China, which was based on road traffic flow, vehicle speed, and carbon emissions. Referring to existing research results to calibrate the value of time, this paper modified the traveler's generalized cost function, including the carbon emissions tax, fuel surcharge and travel time cost, which can be used in the travel impedance model with the consideration of the carbon emissions tax. Then, a method for analyzing urban road network traffic flow distribution was put forward, and a joint traffic distribution model was established, which considered the relationship between private cars and taxis. Finally, this paper took the city of Panjin as an example to analyze the road traffic carbon emissions tax's impact. The results illustrated that the carbon emissions tax has a positive effect on road network flow equilibrium and carbon emission reduction. This paper will have good reference value and practical significance for the calculation and implementation of urban traffic carbon emissions taxes in China.

A new traffic control design method for large networks with signalized intersections

NASA Technical Reports Server (NTRS)

Leininger, G. G.; Colony, D. C.; Seldner, K.

1979-01-01

The paper presents a traffic control design technique for application to large traffic networks with signalized intersections. It is shown that the design method adopts a macroscopic viewpoint to establish a new traffic modelling procedure in which vehicle platoons are subdivided into main stream queues and turning queues. Optimization of the signal splits minimizes queue lengths in the steady state condition and improves traffic flow conditions, from the viewpoint of the traveling public. Finally, an application of the design method to a traffic network with thirty-three signalized intersections is used to demonstrate the effectiveness of the proposed technique.

Estimating Cumulative Traffic Loads, Final Report for Phase 1

DOT National Transportation Integrated Search

2000-07-01

The knowledge of traffic loads is a prerequisite for the pavement analysis process, especially for the development of load-related distress prediction models. Furthermore, the emerging mechanistically based pavement performance models and pavement de...

Calibration of CORSIM models under saturated traffic flow conditions.

DOT National Transportation Integrated Search

2013-09-01

This study proposes a methodology to calibrate microscopic traffic flow simulation models. : The proposed methodology has the capability to calibrate simultaneously all the calibration : parameters as well as demand patterns for any network topology....

Studies of uncontrolled air traffic patterns, phase 1

NASA Technical Reports Server (NTRS)

Baxa, E. G., Jr.; Scharf, L. L.; Ruedger, W. H.; Modi, J. A.; Wheelock, S. L.; Davis, C. M.

1975-01-01

The general aviation air traffic flow patterns at uncontrolled airports are investigated and analyzed and traffic pattern concepts are developed to minimize the midair collision hazard in uncontrolled airspace. An analytical approach to evaluate midair collision hazard probability as a function of traffic densities is established which is basically independent of path structure. Two methods of generating space-time interrelationships between terminal area aircraft are presented; one is a deterministic model to generate pseudorandom aircraft tracks, the other is a statistical model in preliminary form. Some hazard measures are presented for selected traffic densities. It is concluded that the probability of encountering a hazard should be minimized independently of any other considerations and that the number of encounters involving visible-avoidable aircraft should be maximized at the expense of encounters in other categories.

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.

Classification and unification of the microscopic deterministic traffic models.

PubMed

Yang, Bo; Monterola, Christopher

2015-10-01

We identify a universal mathematical structure in microscopic deterministic traffic models (with identical drivers), and thus we show that all such existing models in the literature, including both the two-phase and three-phase models, can be understood as special cases of a master model by expansion around a set of well-defined ground states. This allows any two traffic models to be properly compared and identified. The three-phase models are characterized by the vanishing of leading orders of expansion within a certain density range, and as an example the popular intelligent driver model is shown to be equivalent to a generalized optimal velocity (OV) model. We also explore the diverse solutions of the generalized OV model that can be important both for understanding human driving behaviors and algorithms for autonomous driverless vehicles.

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.

PubMed

Liu, Shi V; Chen, Fu-Lin; Xue, Jianping

2017-12-15

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 land use regression (LUR) models has improved exposure estimation. To better understand the relationship between vehicle emissions and near-road air pollution, we evaluated three traffic density-based indices: Major-Road Density (MRD), All-Traffic Density (ATD) and Heavy-Traffic Density (HTD) which represent the proportions of major roads, major road with annual average daily traffic (AADT), and major road with commercial annual average daily traffic (CAADT) in a buffered area, respectively. We evaluated the potential of these indices as vehicle emission-specific near-road air pollutant indicators by analyzing their correlation with black carbon (BC), a marker for mobile source air pollutants, using measurement data obtained from the Near-road Exposures and Effects of Urban Air Pollutants Study (NEXUS). The average BC concentrations during a day showed variations consistent with changes in traffic volume which were classified into high, medium, and low for the morning rush hours, the evening rush hours, and the rest of the day, respectively. The average correlation coefficients between BC concentrations and MRD, ATD, and HTD, were 0.26, 0.18, and 0.48, respectively, as compared with -0.31 and 0.25 for two commonly used traffic indicators: nearest distance to a major road and total length of the major road. HTD, which includes only heavy-duty diesel vehicles in its traffic count, gives statistically significant correlation coefficients for all near-road distances (50, 100, 150, 200, 250, and 300 m) that were analyzed. Generalized linear model (GLM) analyses show that season, traffic volume, HTD, and distance from major roads are highly related to BC measurements. Our analyses indicate that traffic density parameters may be more specific indicators of near-road BC concentrations for health risk studies. HTD is the best index for reflecting near-road BC concentrations which are influenced mainly by the emissions of heavy-duty diesel engines.

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

PubMed Central

Chen, Fu-Lin; Xue, Jianping

2017-01-01

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 land use regression (LUR) models has improved exposure estimation. To better understand the relationship between vehicle emissions and near-road air pollution, we evaluated three traffic density-based indices: Major-Road Density (MRD), All-Traffic Density (ATD) and Heavy-Traffic Density (HTD) which represent the proportions of major roads, major road with annual average daily traffic (AADT), and major road with commercial annual average daily traffic (CAADT) in a buffered area, respectively. We evaluated the potential of these indices as vehicle emission-specific near-road air pollutant indicators by analyzing their correlation with black carbon (BC), a marker for mobile source air pollutants, using measurement data obtained from the Near-road Exposures and Effects of Urban Air Pollutants Study (NEXUS). The average BC concentrations during a day showed variations consistent with changes in traffic volume which were classified into high, medium, and low for the morning rush hours, the evening rush hours, and the rest of the day, respectively. The average correlation coefficients between BC concentrations and MRD, ATD, and HTD, were 0.26, 0.18, and 0.48, respectively, as compared with −0.31 and 0.25 for two commonly used traffic indicators: nearest distance to a major road and total length of the major road. HTD, which includes only heavy-duty diesel vehicles in its traffic count, gives statistically significant correlation coefficients for all near-road distances (50, 100, 150, 200, 250, and 300 m) that were analyzed. Generalized linear model (GLM) analyses show that season, traffic volume, HTD, and distance from major roads are highly related to BC measurements. Our analyses indicate that traffic density parameters may be more specific indicators of near-road BC concentrations for health risk studies. HTD is the best index for reflecting near-road BC concentrations which are influenced mainly by the emissions of heavy-duty diesel engines. PMID:29244754

Association between light absorption measurements of PM2.5 and distance from heavy traffic roads in the Mexico City metropolitan area.

PubMed

Cortez-Lugo, Marlene; Escamilla-Núñez, Consuelo; Barraza-Villarreal, Albino; Texcalac-Sangrador, José Luis; Chow, Judith; Watson, John; Hernández-Cadena, Leticia; Romieu, Isabelle

2013-04-01

To study the relationship between light absorption measurements of PM2.5 at various distances from heavy traffic roads and diesel vehicle counts in Mexico City. PM2.5 samples were obtained from June 2003-June 2005 in three MCMA regions. Light absorption (b abs) in a subset of PM2.5 samples was determined. We evaluated the effect of distance and diesel vehicle counts to heavy traffic roads on PM2.5 b abs using generalized estimating equation models. Median PM2.5 b abs measurements significantly decrease as distance from heavy traffic roads increases (p<0.002); levels decreased by 7% (CI95% 0.9-14) for each 100 additional meters from heavy traffic roads. Our model predicts that PM2.5 b abs measurements would increase by 20% (CI95% 3-38) as the hourly heavy diesel vehicle count increases by 150 per hour. PM2.5 b abs measurements are significantly associated with distance from motorways and traffic density and therefore can be used to assess human exposure to traffic-related emissions.

Analytical studies on the instabilities of heterogeneous intelligent traffic flow

NASA Astrophysics Data System (ADS)

Ngoduy, D.

2013-10-01

It has been widely reported in literature that a small perturbation in traffic flow such as a sudden deceleration of a vehicle could lead to the formation of traffic jams without a clear bottleneck. These traffic jams are usually related to instabilities in traffic flow. The applications of intelligent traffic systems are a potential solution to reduce the amplitude or to eliminate the formation of such traffic instabilities. A lot of research has been conducted to theoretically study the effect of intelligent vehicles, for example adaptive cruise control vehicles, using either computer simulation or analytical method. However, most current analytical research has only applied to single class traffic flow. To this end, the main topic of this paper is to perform a linear stability analysis to find the stability threshold of heterogeneous traffic flow using microscopic models, particularly the effect of intelligent vehicles on heterogeneous (or multi-class) traffic flow instabilities. The analytical results will show how intelligent vehicle percentages affect the stability of multi-class traffic flow.

Advanced Models and Algorithms for Self-Similar IP Network Traffic Simulation and Performance Analysis

NASA Astrophysics Data System (ADS)

Radev, Dimitar; Lokshina, Izabella

2010-11-01

The paper examines self-similar (or fractal) properties of real communication network traffic data over a wide range of time scales. These self-similar properties are very different from the properties of traditional models based on Poisson and Markov-modulated Poisson processes. Advanced fractal models of sequentional generators and fixed-length sequence generators, and efficient algorithms that are used to simulate self-similar behavior of IP network traffic data are developed and applied. Numerical examples are provided; and simulation results are obtained and analyzed.

Phase II, improved work zone design guidelines and enhanced model of traffic delays in work zones : final report, March 2009.

DOT National Transportation Integrated Search

2009-03-01

This project contains three major parts. In the first part a digital computer simulation model was developed with the aim to model the traffic through a freeway work zone situation. The model was based on the Arena simulation software and used cumula...

Phase II, improved work zone design guidelines and enhanced model of traffic delays in work zones : executive summary report.

DOT National Transportation Integrated Search

2009-03-01

This project contains three major parts. In the first part a digital computer simulation model was developed with the aim to model the traffic through a freeway work zone situation. The model was based on the Arena simulation software and used cumula...

Microscale traffic simulation and emission estimation in a heavily trafficked roundabout in Madrid (Spain).

PubMed

Quaassdorff, Christina; Borge, Rafael; Pérez, Javier; Lumbreras, Julio; de la Paz, David; de Andrés, Juan Manuel

2016-10-01

This paper presents the evaluation of emissions from vehicle operations in a domain of 300m×300m covering a complex urban roundabout with high traffic density in Madrid. Micro-level simulation was successfully applied to estimate the emissions on a scale of meters. Two programs were used: i) VISSIM to simulate the traffic on the square and to compute velocity-time profiles; and ii) VERSIT+micro through ENVIVER that uses VISSIM outputs to compute the related emissions at vehicle level. Data collection was achieved by a measurement campaign obtaining empirical data of vehicle flows and traffic intensities. Twelve simulations of different traffic situations (scenarios) were conducted, representing different hours from several days in a week and the corresponding NOX and PM10 emissions were estimated. The results show a general reduction on average speeds for higher intensities due to braking-acceleration patterns that contribute to increase the average emission factor and, therefore, the total emissions in the domain, especially on weekdays. The emissions are clearly related to traffic volume, although maximum emission scenario does not correspond to the highest traffic intensity due to congestion and variations in fleet composition throughout the day. These results evidence the potential that local measures aimed at alleviating congestion may have in urban areas to reduce emissions. In general, scenario-averaged emission factors estimated with the VISSIM-VERSIT+micro modelling system fitted well those from the average-speed model COPERT, used as a preliminary validation of the results. The largest deviations between these two models occur in those scenarios with more congestion. The design and resolution of the microscale modelling system allow to reflect the impact of actual traffic conditions on driving patterns and related emissions, making it useful for the design of mitigation measures for specific traffic hot-spots. Copyright © 2016 Elsevier B.V. All rights reserved.

Impact on air quality of measures to reduce CO2 emissions from road traffic in Basel, Rotterdam, Xi'an and Suzhou

NASA Astrophysics Data System (ADS)

Keuken, M. P.; Jonkers, S.; Verhagen, H. L. M.; Perez, L.; Trüeb, S.; Okkerse, W.-J.; Liu, J.; Pan, X. C.; Zheng, L.; Wang, H.; Xu, R.; Sabel, C. E.

2014-12-01

Two traffic scenarios to reduce CO2 emissions from road traffic in two European cities (Basel and Rotterdam) and two Chinese cities (Xi'an and Suzhou) were evaluated in terms of their impact on air quality. The two scenarios, one modelling a reduction of private vehicle kilometres driven by 10% on urban streets and the other modelling the introduction of 50% electric-powered private vehicle kilometres on urban streets, were both compared to a scenario following “business-as-usual”: 2020-BAU. The annual average concentrations of NO2, PM2.5, PM10 and elemental carbon (EC) were modelled separately in busy street canyons, near urban motorways and in the remainder of the urban area. It was concluded that traffic-related CO2 emissions in 2020-BAU could be expected to remain at the levels of 2010 in Basel and Rotterdam, while in Xi'an and Suzhou to increase 30-50% due to growth in the traffic volume. Traffic-related CO2 emissions may be reduced by up to 5% and 25%, respectively using the first and second scenarios. Air pollution in the Chinese cities is a factor 3 to 5 higher than in the European cities in 2010 and 2020-BAU. The impact of both CO2 reduction scenarios on air quality in 2020-BAU is limited. In Europe, due to implementation of stringent emission standards in all sectors, air quality is expected to improve at both the urban background and near busy road traffic. In China, the regional background is expected to improve for EC, stabilize for PM2.5 and PM10, and decrease for NO2. The urban background follows this regional trend, while near busy road traffic, air pollution will remain elevated due to the considerable growth in traffic volume. A major constraint for modelling air quality in China is access to the input data required and lack of measurements at ground level for validation.

Time series modeling in traffic safety research.

PubMed

Lavrenz, Steven M; Vlahogianni, Eleni I; Gkritza, Konstantina; Ke, Yue

2018-08-01

The use of statistical models for analyzing traffic safety (crash) data has been well-established. However, time series techniques have traditionally been underrepresented in the corresponding literature, due to challenges in data collection, along with a limited knowledge of proper methodology. In recent years, new types of high-resolution traffic safety data, especially in measuring driver behavior, have made time series modeling techniques an increasingly salient topic of study. Yet there remains a dearth of information to guide analysts in their use. This paper provides an overview of the state of the art in using time series models in traffic safety research, and discusses some of the fundamental techniques and considerations in classic time series modeling. It also presents ongoing and future opportunities for expanding the use of time series models, and explores newer modeling techniques, including computational intelligence models, which hold promise in effectively handling ever-larger data sets. The information contained herein is meant to guide safety researchers in understanding this broad area of transportation data analysis, and provide a framework for understanding safety trends that can influence policy-making. Copyright © 2017 Elsevier Ltd. All rights reserved.

Investigating the role of transportation models in epidemiologic studies of traffic related air pollution and health effects.

PubMed

Shekarrizfard, Maryam; Valois, Marie-France; Goldberg, Mark S; Crouse, Dan; Ross, Nancy; Parent, Marie-Elise; Yasmin, Shamsunnahar; Hatzopoulou, Marianne

2015-07-01

In two earlier case-control studies conducted in Montreal, nitrogen dioxide (NO2), a marker for traffic-related air pollution was found to be associated with the incidence of postmenopausal breast cancer and prostate cancer. These studies relied on a land use regression model (LUR) for NO2 that is commonly used in epidemiologic studies for deriving estimates of traffic-related air pollution. Here, we investigate the use of a transportation model developed during the summer season to generate a measure of traffic emissions as an alternative to the LUR model. Our traffic model provides estimates of emissions of nitrogen oxides (NOx) at the level of individual roads, as does the LUR model. Our main objective was to compare the distribution of the spatial estimates of NOx computed from our transportation model to the distribution obtained from the LUR model. A secondary objective was to compare estimates of risk using these two exposure estimates. We observed that the correlation (spearman) between our two measures of exposure (NO2 and NOx) ranged from less than 0.3 to more than 0.9 across Montreal neighborhoods. The most important factor affecting the "agreement" between the two measures in a specific area was found to be the length of roads. Areas affected by a high level of traffic-related air pollution had a far better agreement between the two exposure measures. A comparison of odds ratios (ORs) obtained from NO2 and NOx used in two case-control studies of breast and prostate cancer, showed that the differences between the ORs associated with NO2 exposure vs NOx exposure differed by 5.2-8.8%. Copyright © 2015 Elsevier Inc. All rights reserved.

Road traffic accidents prediction modelling: An analysis of Anambra State, Nigeria.

PubMed

Ihueze, Chukwutoo C; Onwurah, Uchendu O

2018-03-01

One of the major problems in the world today is the rate of road traffic crashes and deaths on our roads. Majority of these deaths occur in low-and-middle income countries including Nigeria. This study analyzed road traffic crashes in Anambra State, Nigeria with the intention of developing accurate predictive models for forecasting crash frequency in the State using autoregressive integrated moving average (ARIMA) and autoregressive integrated moving average with explanatory variables (ARIMAX) modelling techniques. The result showed that ARIMAX model outperformed the ARIMA (1,1,1) model generated when their performances were compared using the lower Bayesian information criterion, mean absolute percentage error, root mean square error; and higher coefficient of determination (R-Squared) as accuracy measures. The findings of this study reveal that incorporating human, vehicle and environmental related factors in time series analysis of crash dataset produces a more robust predictive model than solely using aggregated crash count. This study contributes to the body of knowledge on road traffic safety and provides an approach to forecasting using many human, vehicle and environmental factors. The recommendations made in this study if applied will help in reducing the number of road traffic crashes in Nigeria. Copyright © 2017 Elsevier Ltd. All rights reserved.

Assimilating Eulerian and Lagrangian data in traffic-flow models

NASA Astrophysics Data System (ADS)

Xia, Chao; Cochrane, Courtney; DeGuire, Joseph; Fan, Gaoyang; Holmes, Emma; McGuirl, Melissa; Murphy, Patrick; Palmer, Jenna; Carter, Paul; Slivinski, Laura; Sandstede, Björn

2017-05-01

Data assimilation of traffic flow remains a challenging problem. One difficulty is that data come from different sources ranging from stationary sensors and camera data to GPS and cell phone data from moving cars. Sensors and cameras give information about traffic density, while GPS data provide information about the positions and velocities of individual cars. Previous methods for assimilating Lagrangian data collected from individual cars relied on specific properties of the underlying computational model or its reformulation in Lagrangian coordinates. These approaches make it hard to assimilate both Eulerian density and Lagrangian positional data simultaneously. In this paper, we propose an alternative approach that allows us to assimilate both Eulerian and Lagrangian data. We show that the proposed algorithm is accurate and works well in different traffic scenarios and regardless of whether ensemble Kalman or particle filters are used. We also show that the algorithm is capable of estimating parameters and assimilating real traffic observations and synthetic observations obtained from microscopic models.

Characteristics of traffic flow at a non-signalized intersection in the framework of game theory

NASA Astrophysics Data System (ADS)

Fan, Hongqiang; Jia, Bin; Tian, Junfang; Yun, Lifen

2014-12-01

At a non-signalized intersection, some vehicles violate the traffic rules to pass the intersection as soon as possible. These behaviors may cause many traffic conflicts even traffic accidents. In this paper, a simulation model is proposed to research the effects of these behaviors at a non-signalized intersection. Vehicle’s movement is simulated by the cellular automaton (CA) model. The game theory is introduced for simulating the intersection dynamics. Two types of driver participate the game process: cooperator (C) and defector (D). The cooperator obey the traffic rules, but the defector does not. A transition process may occur when the cooperator is waiting before the intersection. The critical value of waiting time follows the Weibull distribution. One transition regime is found in the phase diagram. The simulation results illustrate the applicability of the proposed model and reveal a number of interesting insights into the intersection management, including that the existence of defectors is benefit for the capacity of intersection, but also reduce the safety of intersection.

Future Air Traffic Growth and Schedule Model User's Guide

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.

Modeling the coevolution of topology and traffic on weighted technological networks

NASA Astrophysics Data System (ADS)

Xie, Yan-Bo; Wang, Wen-Xu; Wang, Bing-Hong

2007-02-01

For many technological networks, the network structures and the traffic taking place on them mutually interact. The demands of traffic increment spur the evolution and growth of the networks to maintain their normal and efficient functioning. In parallel, a change of the network structure leads to redistribution of the traffic. In this paper, we perform an extensive numerical and analytical study, extending results of Wang [Phys. Rev. Lett. 94, 188702 (2005)]. By introducing a general strength-coupling interaction driven by the traffic increment between any pair of vertices, our model generates networks of scale-free distributions of strength, weight, and degree. In particular, the obtained nonlinear correlation between vertex strength and degree, and the disassortative property demonstrate that the model is capable of characterizing weighted technological networks. Moreover, the generated graphs possess both dense clustering structures and an anticorrelation between vertex clustering and degree, which are widely observed in real-world networks. The corresponding theoretical predictions are well consistent with simulation results.

Phase transition of a new lattice hydrodynamic model with consideration of on-ramp and off-ramp

NASA Astrophysics Data System (ADS)

Zhang, Geng; Sun, Di-hua; Zhao, Min

2018-01-01

A new traffic lattice hydrodynamic model with consideration of on-ramp and off-ramp is proposed in this paper. The influence of on-ramp and off-ramp on the stability of the main road is uncovered by theoretical analysis and computer simulation. Through linear stability theory, the neutral stability condition of the new model is obtained and the results show that the unstable region in the phase diagram is enlarged by considering the on-ramp effect but shrunk with consideration of the off-ramp effect. The mKdV equation near the critical point is derived via nonlinear reductive perturbation method and the occurrence of traffic jamming transition can be described by the kink-antikink soliton solution of the mKdV equation. From the simulation results of space-time evolution of traffic density waves, it is shown that the on-ramp can worsen the traffic stability of the main road but off-ramp is positive in stabilizing the traffic flow of the main road.

Road traffic air and noise pollution exposure assessment - A review of tools and techniques.

PubMed

Khan, Jibran; Ketzel, Matthias; Kakosimos, Konstantinos; Sørensen, Mette; Jensen, Steen Solvang

2018-09-01

Road traffic induces air and noise pollution in urban environments having negative impacts on human health. Thus, estimating exposure to road traffic air and noise pollution (hereafter, air and noise pollution) is important in order to improve the understanding of human health outcomes in epidemiological studies. The aims of this review are (i) to summarize current practices of modelling and exposure assessment techniques for road traffic air and noise pollution (ii) to highlight the potential of existing tools and techniques for their combined exposure assessment for air and noise together with associated challenges, research gaps and priorities. The study reviews literature about air and noise pollution from urban road traffic, including other relevant characteristics such as the employed dispersion models, Geographic Information System (GIS)-based tool, spatial scale of exposure assessment, study location, sample size, type of traffic data and building geometry information. Deterministic modelling is the most frequently used assessment technique for both air and noise pollution of short-term and long-term exposure. We observed a larger variety among air pollution models as compared to the applied noise models. Correlations between air and noise pollution vary significantly (0.05-0.74) and are affected by several parameters such as traffic attributes, building attributes and meteorology etc. Buildings act as screens for the dispersion of pollution, but the reduction effect is much larger for noise than for air pollution. While, meteorology has a greater influence on air pollution levels as compared to noise, although also important for noise pollution. There is a significant potential for developing a standard tool to assess combined exposure of traffic related air and noise pollution to facilitate health related studies. GIS, due to its geographic nature, is well established and has a significant capability to simultaneously address both exposures. Copyright © 2018 Elsevier B.V. All rights reserved.

A modeling framework for characterizing near-road air pollutant concentration at community scales

EPA Science Inventory

In this study, we combine information from transportation network, traffic emissions, and dispersion model to develop a framework to inform exposure estimates for traffic-related air pollutants (TRAPs) with a high spatial resolution. A Research LINE source dispersion model (R-LIN...

Modeling DNP3 Traffic Characteristics of Field Devices in SCADA Systems of the Smart Grid

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

Yang, Huan; Cheng, Liang; Chuah, Mooi Choo

In the generation, transmission, and distribution sectors of the smart grid, intelligence of field devices is realized by programmable logic controllers (PLCs). Many smart-grid subsystems are essentially cyber-physical energy systems (CPES): For instance, the power system process (i.e., the physical part) within a substation is monitored and controlled by a SCADA network with hosts running miscellaneous applications (i.e., the cyber part). To study the interactions between the cyber and physical components of a CPES, several co-simulation platforms have been proposed. However, the network simulators/emulators of these platforms do not include a detailed traffic model that takes into account the impactsmore » of the execution model of PLCs on traffic characteristics. As a result, network traces generated by co-simulation only reveal the impacts of the physical process on the contents of the traffic generated by SCADA hosts, whereas the distinction between PLCs and computing nodes (e.g., a hardened computer running a process visualization application) has been overlooked. To generate realistic network traces using co-simulation for the design and evaluation of applications relying on accurate traffic profiles, it is necessary to establish a traffic model for PLCs. In this work, we propose a parameterized model for PLCs that can be incorporated into existing co-simulation platforms. We focus on the DNP3 subsystem of slave PLCs, which automates the processing of packets from the DNP3 master. To validate our approach, we extract model parameters from both the configuration and network traces of real PLCs. Simulated network traces are generated and compared against those from PLCs. Our evaluation shows that our proposed model captures the essential traffic characteristics of DNP3 slave PLCs, which can be used to extend existing co-simulation platforms and gain further insights into the behaviors of CPES.« less

Adaptive route choice modeling in uncertain traffic networks with real-time information.

DOT National Transportation Integrated Search

2013-03-01

The objective of the research is to study travelers' route choice behavior in uncertain traffic networks : with real-time information. The research is motivated by two observations of the traffic system: 1) : the system is inherently uncertain with r...

Simulations of Highway Traffic With Various Degrees of Automation

DOT National Transportation Integrated Search

1996-01-01

A traffic simulator to study highway traffic under various degrees of automation is being developed at Argonne National Laboratory. The key components of this simulator include a global and a local Expert Driver Model, a human factor study and a grap...

Congestion control strategy on complex network with privilege traffic

NASA Astrophysics Data System (ADS)

Li, Shi-Bao; He, Ya; Liu, Jian-Hang; Zhang, Zhi-Gang; Huang, Jun-Wei

The congestion control of traffic is one of the most important studies in complex networks. In the previous congestion algorithms, all the network traffic is assumed to have the same priority, and the privilege of traffic is ignored. In this paper, a privilege and common traffic congestion control routing strategy (PCR) based on the different priority of traffic is proposed, which can be devised to cope with the different traffic congestion situations. We introduce the concept of privilege traffic in traffic dynamics for the first time and construct a new traffic model which taking into account requirements with different priorities. Besides, a new factor Ui is introduced by the theoretical derivation to characterize the interaction between different traffic routing selection, furthermore, Ui is related to the network throughput. Since the joint optimization among different kinds of traffic is accomplished by PCR, the maximum value of Ui can be significantly reduced and the network performance can be improved observably. The simulation results indicate that the network throughput with PCR has a better performance than the other strategies. Moreover, the network capacity is improved by 25% at least. Additionally, the network throughput is also influenced by privilege traffic number and traffic priority.

Top-down quantification of NOx emissions from traffic in an urban area using a high-resolution regional atmospheric chemistry model

NASA Astrophysics Data System (ADS)

Kuik, Friderike; Kerschbaumer, Andreas; Lauer, Axel; Lupascu, Aurelia; von Schneidemesser, Erika; Butler, Tim M.

2018-06-01

With NO2 limit values being frequently exceeded in European cities, complying with the European air quality regulations still poses a problem for many cities. Traffic is typically a major source of NOx emissions in urban areas. High-resolution chemistry transport modelling can help to assess the impact of high urban NOx emissions on air quality inside and outside of urban areas. However, many modelling studies report an underestimation of modelled NOx and NO2 compared with observations. Part of this model bias has been attributed to an underestimation of NOx emissions, particularly in urban areas. This is consistent with recent measurement studies quantifying underestimations of urban NOx emissions by current emission inventories, identifying the largest discrepancies when the contribution of traffic NOx emissions is high. This study applies a high-resolution chemistry transport model in combination with ambient measurements in order to assess the potential underestimation of traffic NOx emissions in a frequently used emission inventory. The emission inventory is based on officially reported values and the Berlin-Brandenburg area in Germany is used as a case study. The WRF-Chem model is used at a 3 km × 3 km horizontal resolution, simulating the whole year of 2014. The emission data are downscaled from an original resolution of ca. 7 km × 7 km to a resolution of 1 km × 1 km. An in-depth model evaluation including spectral decomposition of observed and modelled time series and error apportionment suggests that an underestimation in traffic emissions is likely one of the main causes of the bias in modelled NO2 concentrations in the urban background, where NO2 concentrations are underestimated by ca. 8 µg m-3 (-30 %) on average over the whole year. Furthermore, a diurnal cycle of the bias in modelled NO2 suggests that a more realistic treatment of the diurnal cycle of traffic emissions might be needed. Model problems in simulating the correct mixing in the urban planetary boundary layer probably play an important role in contributing to the model bias, particularly in summer. Also taking into account this and other possible sources of model bias, a correction factor for traffic NOx emissions of ca. 3 is estimated for weekday daytime traffic emissions in the core urban area, which corresponds to an overall underestimation of traffic NOx emissions in the core urban area of ca. 50 %. Sensitivity simulations for the months of January and July using the calculated correction factor show that the weekday model bias can be improved from -8.8 µg m-3 (-26 %) to -5.4 µg m-3 (-16 %) in January on average in the urban background, and -10.3 µg m-3 (-46 %) to -7.6 µg m-3 (-34 %) in July. In addition, the negative bias of weekday NO2 concentrations downwind of the city in the rural and suburban background can be reduced from -3.4 µg m-3 (-12 %) to -1.2 µg m-3 (-4 %) in January and from -3.0 µg m-3 (-22 %) to -1.9 µg m-3 (-14 %) in July. The results and their consistency with findings from other studies suggest that more research is needed in order to more accurately understand the spatial and temporal variability in real-world NOx emissions from traffic, and apply this understanding to the inventories used in high-resolution chemical transport models.

The Airspace Concepts Evaluation System Architecture and System Plant

NASA Technical Reports Server (NTRS)

Windhorst, Robert; Meyn, Larry; Manikonda, Vikram; Carlos, Patrick; Capozzi, Brian

2006-01-01

The Airspace Concepts Evaluation System is a simulation of the National Airspace System. It includes models of flights, airports, airspaces, air traffic controls, traffic flow managements, and airline operation centers operating throughout the United States. It is used to predict system delays in response to future capacity and demand scenarios and perform benefits assessments of current and future airspace technologies and operational concepts. Facilitation of these studies requires that the simulation architecture supports plug and play of different air traffic control, traffic flow management, and airline operation center models and multi-fidelity modeling of flights, airports, and airspaces. The simulation is divided into two parts that are named, borrowing from classical control theory terminology, control and plant. The control consists of air traffic control, traffic flow management, and airline operation center models, and the plant consists of flight, airport, and airspace models. The plant can run open loop, in the absence of the control. However, undesired affects, such as conflicts and over congestions in the airspaces and airports, can occur. Different controls are applied, "plug and played", to the plant. A particular control is evaluated by analyzing how well it managed conflicts and congestions. Furthermore, the terminal area plants consist of models of airports and terminal airspaces. Each model consists of a set of nodes and links which are connected by the user to form a network. Nodes model runways, fixes, taxi intersections, gates, and/or other points of interest, and links model taxiways, departure paths, and arrival paths. Metering, flow distribution, and sequencing functions can be applied at nodes. Different fidelity model of how a flight transits are can be used by links. The fidelity of the model can be adjusted by the user by either changing the complexity of the node/link network-or the way that the link models how the flights transit from one node to the other.

Traffic sign classification with dataset augmentation and convolutional neural network

NASA Astrophysics Data System (ADS)

Tang, Qing; Kurnianggoro, Laksono; Jo, Kang-Hyun

2018-04-01

This paper presents a method for traffic sign classification using a convolutional neural network (CNN). In this method, firstly we transfer a color image into grayscale, and then normalize it in the range (-1,1) as the preprocessing step. To increase robustness of classification model, we apply a dataset augmentation algorithm and create new images to train the model. To avoid overfitting, we utilize a dropout module before the last fully connection layer. To assess the performance of the proposed method, the German traffic sign recognition benchmark (GTSRB) dataset is utilized. Experimental results show that the method is effective in classifying traffic signs.

Hysteresis phenomena of the intelligent driver model for traffic flow

NASA Astrophysics Data System (ADS)

Dahui, Wang; Ziqiang, Wei; Ying, Fan

2007-07-01

We present hysteresis phenomena of the intelligent driver model for traffic flow in a circular one-lane roadway. We show that the microscopic structure of traffic flow is dependent on its initial state by plotting the fraction of congested vehicles over the density, which shows a typical hysteresis loop, and by investigating the trajectories of vehicles on the velocity-over-headway plane. We find that the trajectories of vehicles on the velocity-over-headway plane, which usually show a hysteresis loop, include multiple loops. We also point out the relations between these hysteresis loops and the congested jams or high-density clusters in traffic flow.

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.

Web application and database modeling of traffic impact analysis using Google Maps

NASA Astrophysics Data System (ADS)

Yulianto, Budi; Setiono

2017-06-01

Traffic impact analysis (TIA) is a traffic study that aims at identifying the impact of traffic generated by development or change in land use. In addition to identifying the traffic impact, TIA is also equipped with mitigation measurement to minimize the arising traffic impact. TIA has been increasingly important since it was defined in the act as one of the requirements in the proposal of Building Permit. The act encourages a number of TIA studies in various cities in Indonesia, including Surakarta. For that reason, it is necessary to study the development of TIA by adopting the concept Transportation Impact Control (TIC) in the implementation of the TIA standard document and multimodal modeling. It includes TIA's standardization for technical guidelines, database and inspection by providing TIA checklists, monitoring and evaluation. The research was undertaken by collecting the historical data of junctions, modeling of the data in the form of relational database, building a user interface for CRUD (Create, Read, Update and Delete) the TIA data in the form of web programming with Google Maps libraries. The result research is a system that provides information that helps the improvement and repairment of TIA documents that exist today which is more transparent, reliable and credible.

Personality and attitudes as predictors of risky driving among older drivers.

PubMed

Lucidi, Fabio; Mallia, Luca; Lazuras, Lambros; Violani, Cristiano

2014-11-01

Although there are several studies on the effects of personality and attitudes on risky driving among young drivers, related research in older drivers is scarce. The present study assessed a model of personality-attitudes-risky driving in a large sample of active older drivers. A cross-sectional design was used, and structured and anonymous questionnaires were completed by 485 older Italian drivers (Mean age=68.1, SD=6.2, 61.2% males). The measures included personality traits, attitudes toward traffic safety, risky driving (errors, lapses, and traffic violations), and self-reported crash involvement and number of issued traffic tickets in the last 12 months. Structural equation modeling showed that personality traits predicted both directly and indirectly traffic violations, errors, and lapses. More positive attitudes toward traffic safety negatively predicted risky driving. In turn, risky driving was positively related to self-reported crash involvement and higher number of issued traffic tickets. Our findings suggest that theoretical models developed to account for risky driving of younger drivers may also apply in the older drivers, and accordingly be used to inform safe driving interventions for this age group. Copyright © 2014 Elsevier Ltd. All rights reserved.

Estimation of carbon dioxide emissions per urban center link unit using data collected by the Advanced Traffic Information System in Daejeon, Korea

NASA Astrophysics Data System (ADS)

Ryu, B. Y.; Jung, H. J.; Bae, S. H.; Choi, C. U.

2013-12-01

CO2 emissions on roads in urban centers substantially affect global warming. It is important to quantify CO2 emissions in terms of the link unit in order to reduce these emissions on the roads. Therefore, in this study, we utilized real-time traffic data and attempted to develop a methodology for estimating CO2 emissions per link unit. Because of the recent development of the vehicle-to-infrastructure (V2I) communication technology, data from probe vehicles (PVs) can be collected and speed per link unit can be calculated. Among the existing emission calculation methodologies, mesoscale modeling, which is a representative modeling measurement technique, requires speed and traffic data per link unit. As it is not feasible to install fixed detectors at every link for traffic data collection, in this study, we developed a model for traffic volume estimation by utilizing the number of PVs that can be additionally collected when the PV data are collected. Multiple linear regression and an artificial neural network (ANN) were used for estimating the traffic volume. The independent variables and input data for each model are the number of PVs, travel time index (TTI), the number of lanes, and time slots. The result from the traffic volume estimate model shows that the mean absolute percentage error (MAPE) of the ANN is 18.67%, thus proving that it is more effective. The ANN-based traffic volume estimation served as the basis for the calculation of emissions per link unit. The daily average emissions for Daejeon, where this study was based, were 2210.19 ton/day. By vehicle type, passenger cars accounted for 71.28% of the total emissions. By road, Gyeryongro emitted 125.48 ton/day, accounting for 5.68% of the total emission, the highest percentage of all roads. In terms of emissions per kilometer, Hanbatdaero had the highest emission volume, with 7.26 ton/day/km on average. This study proves that real-time traffic data allow an emissions estimate in terms of the link unit. Furthermore, an analysis of CO2 emissions can support traffic management to make decisions related to the reduction of carbon emissions.

AMPO Travel Modeling Working Group Meeting on Dynamic Traffic Assignment

DOT National Transportation Integrated Search

2016-03-01

On December 17-18, 2015, the Association of Metropolitan Planning Organizations (AMPO) convened a travel modeling working group meeting for the purpose of discussing Dynamic Traffic Assignment (DTA). Participants discussed the uses of DTA, challenges...

Validation of FHWA's traffic noise model (TNM) : phase 1

DOT National Transportation Integrated Search

2002-08-01

The Volpe Center Acoustics Facility, in support of the Federal Highway Administration (FHWA) and the California Department of Transportation (Caltrans), has been conducting a study to quantify and assess the accuracy of FHWAs Traffic Noise Model (...

Two-lane traffic-flow model with an exact steady-state solution.

PubMed

Kanai, Masahiro

2010-12-01

We propose a stochastic cellular-automaton model for two-lane traffic flow based on the misanthrope process in one dimension. The misanthrope process is a stochastic process allowing for an exact steady-state solution; hence, we have an exact flow-density diagram for two-lane traffic. In addition, we introduce two parameters that indicate, respectively, driver's driving-lane preference and passing-lane priority. Due to the additional parameters, the model shows a deviation of the density ratio for driving-lane use and a biased lane efficiency in flow. Then, a mean-field approach explicitly describes the asymmetric flow by the hop rates, the driving-lane preference, and the passing-lane priority. Meanwhile, the simulation results are in good agreement with an observational data, and we thus estimate these parameters. We conclude that the proposed model successfully produces two-lane traffic flow particularly with the driving-lane preference and the passing-lane priority.

Queuing theory models for computer networks

NASA Technical Reports Server (NTRS)

Galant, David C.

1989-01-01

A set of simple queuing theory models which can model the average response of a network of computers to a given traffic load has been implemented using a spreadsheet. The impact of variations in traffic patterns and intensities, channel capacities, and message protocols can be assessed using them because of the lack of fine detail in the network traffic rates, traffic patterns, and the hardware used to implement the networks. A sample use of the models applied to a realistic problem is included in appendix A. Appendix B provides a glossary of terms used in this paper. This Ames Research Center computer communication network is an evolving network of local area networks (LANs) connected via gateways and high-speed backbone communication channels. Intelligent planning of expansion and improvement requires understanding the behavior of the individual LANs as well as the collection of networks as a whole.

An epidemiological survey on road traffic crashes in Iran: application of the two logistic regression models.

PubMed

Bakhtiyari, Mahmood; Mehmandar, Mohammad Reza; Mirbagheri, Babak; Hariri, Gholam Reza; Delpisheh, Ali; Soori, Hamid

2014-01-01

Risk factors of human-related traffic crashes are the most important and preventable challenges for community health due to their noteworthy burden in developing countries in particular. The present study aims to investigate the role of human risk factors of road traffic crashes in Iran. Through a cross-sectional study using the COM 114 data collection forms, the police records of almost 600,000 crashes occurred in 2010 are investigated. The binary logistic regression and proportional odds regression models are used. The odds ratio for each risk factor is calculated. These models are adjusted for known confounding factors including age, sex and driving time. The traffic crash reports of 537,688 men (90.8%) and 54,480 women (9.2%) are analysed. The mean age is 34.1 ± 14 years. Not maintaining eyes on the road (53.7%) and losing control of the vehicle (21.4%) are the main causes of drivers' deaths in traffic crashes within cities. Not maintaining eyes on the road is also the most frequent human risk factor for road traffic crashes out of cities. Sudden lane excursion (OR = 9.9, 95% CI: 8.2-11.9) and seat belt non-compliance (OR = 8.7, CI: 6.7-10.1), exceeding authorised speed (OR = 17.9, CI: 12.7-25.1) and exceeding safe speed (OR = 9.7, CI: 7.2-13.2) are the most significant human risk factors for traffic crashes in Iran. The high mortality rate of 39 people for every 100,000 population emphasises on the importance of traffic crashes in Iran. Considering the important role of human risk factors in traffic crashes, struggling efforts are required to control dangerous driving behaviours such as exceeding speed, illegal overtaking and not maintaining eyes on the road.

No evidence of a threshold in traffic volume affecting road-kill mortality at a large spatio-temporal scale

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

Grilo, Clara, E-mail: clarabentesgrilo@gmail.com; Centro Brasileiro de Estudos em Ecologia de Estradas, Departamento de Biologia, Universidade Federal de Lavras, Campus Universitário, 37200-000 Lavras, Minas Gerais; Ferreira, Flavio Zanchetta

Previous studies have found that the relationship between wildlife road mortality and traffic volume follows a threshold effect on low traffic volume roads. We aimed at evaluating the response of several species to increasing traffic intensity on highways over a large geographic area and temporal period. We used data of four terrestrial vertebrate species with different biological and ecological features known by their high road-kill rates: the barn owl (Tyto alba), hedgehog (Erinaceus europaeus), red fox (Vulpes vulpes) and European rabbit (Oryctolagus cuniculus). Additionally, we checked whether road-kill likelihood varies when traffic patterns depart from the average. We used annualmore » average daily traffic (AADT) and road-kill records observed along 1000 km of highways in Portugal over seven consecutive years (2003–2009). We fitted candidate models using Generalized Linear Models with a binomial distribution through a sample unit of 1 km segments to describe the effect of traffic on the probability of finding at least one victim in each segment during the study. We also assigned for each road-kill record the traffic of that day and the AADT on that year to test for differences using Paired Student's t-test. Mortality risk declined significantly with traffic volume but varied among species: the probability of finding road-killed red foxes and rabbits occurs up to moderate traffic volumes (< 20,000 AADT) whereas barn owls and hedgehogs occurred up to higher traffic volumes (40,000 AADT). Perception of risk may explain differences in responses towards high traffic highway segments. Road-kill rates did not vary significantly when traffic intensity departed from the average. In summary, we did not find evidence of traffic thresholds for the analysed species and traffic intensities. We suggest mitigation measures to reduce mortality be applied in particular on low traffic roads (< 5000 AADT) while additional measures to reduce barrier effects should take into account species-specific behavioural traits. - Highlights: • Traffic and road-kills were analysed along 1000 km of highways over seven years. • Mortality risk declined significantly with traffic volume. • Perception of risk may explain different responses towards high traffic sections. • Reducing barrier effects should take into account species behavioural traits.« less

Development and Evaluation of Model Algorithms to Account for Chemical Transformation in the Nearroad Environment

EPA Science Inventory

We describe the development and evaluation of two new model algorithms for NOx chemistry in the R-LINE near-road dispersion model for traffic sources. With increased urbanization, there is increased mobility leading to higher amount of traffic related activity on a global scale. ...

Effective environmental factors on geographical distribution of traffic accidents on pedestrians, downtown Tehran city

PubMed Central

Moradi, Ali; Soori, Hamid; Kavousi, Amir; Eshghabadi, Farshid; Nematollahi, Shahrzad; Zeini, Salahdien

2017-01-01

Introduction: 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. Methods: 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 R2. Results: 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. Conclusion: 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. PMID:28660163

Using mobile probes to inform and measure the effectiveness of traffic control strategies on urban networks.

DOT National Transportation Integrated Search

2015-07-01

Urban traffic congestion is a problem that plagues many cities in the United States. Testing strategies to alleviate this : congestion is especially challenging due to the difficulty of modeling complex urban traffic networks. However, recent work ha...

Determination of network origin-destination matrices using partial link traffic counts and virtual sensor information in an integrated corridor management framework.

DOT National Transportation Integrated Search

2014-04-01

Trip origin-destination (O-D) demand matrices are critical components in transportation network : modeling, and provide essential information on trip distributions and corresponding spatiotemporal : traffic patterns in traffic zones in vehicular netw...

Operational improvements at traffic circles : safety analysis, final report, December 2008.

DOT National Transportation Integrated Search

2008-12-01

The purpose of this study was to improve the safety and operation at three traffic circles in New : Jersey. To do this, data were collected at the traffic circles to allow researchers to model the : circles using the PARAMICS software simulation pack...

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.

Random parameter models for accident prediction on two-lane undivided highways in India.

PubMed

Dinu, R R; Veeraragavan, A

2011-02-01

Generalized linear modeling (GLM), with the assumption of Poisson or negative binomial error structure, has been widely employed in road accident modeling. A number of explanatory variables related to traffic, road geometry, and environment that contribute to accident occurrence have been identified and accident prediction models have been proposed. The accident prediction models reported in literature largely employ the fixed parameter modeling approach, where the magnitude of influence of an explanatory variable is considered to be fixed for any observation in the population. Similar models have been proposed for Indian highways too, which include additional variables representing traffic composition. The mixed traffic on Indian highways comes with a lot of variability within, ranging from difference in vehicle types to variability in driver behavior. This could result in variability in the effect of explanatory variables on accidents across locations. Random parameter models, which can capture some of such variability, are expected to be more appropriate for the Indian situation. The present study is an attempt to employ random parameter modeling for accident prediction on two-lane undivided rural highways in India. Three years of accident history, from nearly 200 km of highway segments, is used to calibrate and validate the models. The results of the analysis suggest that the model coefficients for traffic volume, proportion of cars, motorized two-wheelers and trucks in traffic, and driveway density and horizontal and vertical curvatures are randomly distributed across locations. The paper is concluded with a discussion on modeling results and the limitations of the present study. Copyright © 2010 Elsevier Ltd. All rights reserved.

The October 1973 expendable launch vehicle traffic model, revision 2

NASA Technical Reports Server (NTRS)

1974-01-01

Traffic model data for current expendable launch vehicles (assuming no space shuttle) for calendar years 1980 through 1991 are presented along with some supporting and summary data. This model was based on a payload program equivalent in scientific return to the October 1973 NASA Payload Model, the NASA estimated non NASA/non DoD Payload Model, and the 1971 DoD Mission Model.

The October 1973 space shuttle traffic model, revision 2

NASA Technical Reports Server (NTRS)

1974-01-01

Traffic model data for the space shuttle for calendar years 1980 through 1991 are presented along with some supporting and summary data. This model was developed from the 1973 NASA Payload Model, dated October 1973, and the NASA estimate of the 1973 Non-NASA/Non-DoD Payload Model. The estimates for the DoD flights included are based on the 1971 DoD Mission Model.

Analysis of vehicular traffic flow in the major areas of Kuala Lumpur utilizing open-traffic

NASA Astrophysics Data System (ADS)

Manogaran, Saargunawathy; Ali, Muhammad; Yusof, Kamaludin Mohamad; Suhaili, Ramdhan

2017-09-01

Vehicular traffic congestion occurs when a large number of drivers are overcrowded on the road and the traffic flow does not run smoothly. Traffic congestion causes chaos on the road and interruption to daily activities of users. Time consumed on road give lots of negative effects on productivity, social behavior, environmental and cost to economy. Congestion is worsens and leads to havoc during the emergency such as flood, accidents, road maintenance and etc., where behavior of traffic flow is always unpredictable and uncontrollable. Real-time and historical traffic data are critical inputs for most traffic flow analysis applications. Researcher attempt to predict traffic using simulations as there is no exact model of traffic flow exists due to its high complexity. Open Traffic is an open source platform available for traffic data analysis linked to Open Street Map (OSM). This research is aimed to study and understand the Open Traffic platform. The real-time traffic flow pattern in Kuala Lumpur area was successfully been extracted and analyzed using Open Traffic. It was observed that the congestion occurs on every major road in Kuala Lumpur and most of it owes to the offices and the economic and commercial centers during rush hours. At some roads the congestion occurs at night due to the tourism activities.

Traffic analysis toolbox volume XIII : integrated corridor management analysis, modeling, and simulation guide

DOT National Transportation Integrated Search

2017-02-01

As part of the Federal Highway Administration (FHWA) Traffic Analysis Toolbox (Volume XIII), this guide was designed to help corridor stakeholders implement the Integrated Corridor Management (ICM) Analysis, Modeling, and Simulation (AMS) methodology...

FHWA Traffic Noise Model (TNM) pavement effects implementation study : progress report 1

DOT National Transportation Integrated Search

2012-01-31

The Volpe Center Acoustics Facility, in support of the Federal Highway Administration (FHWA), investigated the implementation of pavement effects in the FHWA Traffic Noise Model (TNM). Three options were considered, resulting in the recommendation of...

Highway Traffic Simulations on Multi-Processor Computers

DOT National Transportation Integrated Search

1997-01-01

A computer model has been developed to simulate highway traffic for various degrees of automation with a high degree of fidelity in regard to driver control and vehicle characteristics. The model simulates vehicle maneuvering in a multi-lane highway ...

Traffic analysis toolbox volume XIII : integrated corridor management analysis, modeling, and simulation guide.

DOT National Transportation Integrated Search

2017-02-01

As part of the Federal Highway Administration (FHWA) Traffic Analysis Toolbox (Volume XIII), this guide was designed to help corridor stakeholders implement the Integrated Corridor Management (ICM) Analysis, Modeling, and Simulation (AMS) methodology...

Operational improvements at traffic circles : final report, December 2008.

DOT National Transportation Integrated Search

2008-12-01

This study deals with the development of a credible and valid simulation model of the Collingwood, : Brooklawn, and Asbury traffic circles in New Jersey. These simulation models are used to evaluate : various geometric and operational improvement alt...

Identifying the Safety Factors over Traffic Signs in State Roads using a Panel Quantile Regression Approach.

PubMed

Šarić, Željko; Xu, Xuecai; Duan, Li; Babić, Darko

2018-06-20

This study intended to investigate the interactions between accident rate and traffic signs in state roads located in Croatia, and accommodate the heterogeneity attributed to unobserved factors. The data from 130 state roads between 2012 and 2016 were collected from Traffic Accident Database System maintained by the Republic of Croatia Ministry of the Interior. To address the heterogeneity, a panel quantile regression model was proposed, in which quantile regression model offers a more complete view and a highly comprehensive analysis of the relationship between accident rate and traffic signs, while the panel data model accommodates the heterogeneity attributed to unobserved factors. Results revealed that (1) low visibility of material damage (MD) and death or injured (DI) increased the accident rate; (2) the number of mandatory signs and the number of warning signs were more likely to reduce the accident rate; (3)average speed limit and the number of invalid traffic signs per km exhibited a high accident rate. To our knowledge, it's the first attempt to analyze the interactions between accident consequences and traffic signs by employing a panel quantile regression model; by involving the visibility, the present study demonstrates that the low visibility causes a relatively higher risk of MD and DI; It is noteworthy that average speed limit corresponds with accident rate positively; The number of mandatory signs and the number of warning signs are more likely to reduce the accident rate; The number of invalid traffic signs per km are significant for accident rate, thus regular maintenance should be kept for a safer roadway environment.

Enhanced TCAS 2/CDTI traffic Sensor digital simulation model and program description

NASA Technical Reports Server (NTRS)

Goka, T.

1984-01-01

Digital simulation models of enhanced TCAS 2/CDTI traffic sensors are developed, based on actual or projected operational and performance characteristics. Two enhanced Traffic (or Threat) Alert and Collision Avoidance Systems are considered. A digital simulation program is developed in FORTRAN. The program contains an executive with a semireal time batch processing capability. The simulation program can be interfaced with other modules with a minimum requirement. Both the traffic sensor and CAS logic modules are validated by means of extensive simulation runs. Selected validation cases are discussed in detail, and capabilities and limitations of the actual and simulated systems are noted. The TCAS systems are not specifically intended for Cockpit Display of Traffic Information (CDTI) applications. These systems are sufficiently general to allow implementation of CDTI functions within the real systems' constraints.

Suitability of Synthetic Driving Profiles from Traffic Micro-Simulation for Real-World Energy Analysis: Preprint

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

Hou, Yunfei; Wood, Eric; Burton, Evan

A shift towards increased levels of driving automation is generally expected to result in improved safety and traffic congestion outcomes. However, little empirical data exists to estimate the impact that automated driving could have on energy consumption and greenhouse gas emissions. In the absence of empirical data on differences between drive cycles from present day vehicles (primarily operated by humans) and future vehicles (partially or fully operated by computers) one approach is to model both situations over identical traffic conditions. Such an exercise requires traffic micro-simulation to not only accurately model vehicle operation under high levels of automation, but alsomore » (and potentially more challenging) vehicle operation under present day human drivers. This work seeks to quantify the ability of a commercial traffic micro-simulation program to accurately model real-world drive cycles in vehicles operated primarily by humans in terms of driving speed, acceleration, and simulated fuel economy. Synthetic profiles from models of freeway and arterial facilities near Atlanta, Georgia, are compared to empirical data collected from real-world drivers on the same facilities. Empirical and synthetic drive cycles are then simulated in a powertrain efficiency model to enable comparison on the basis of fuel economy. Synthetic profiles from traffic micro-simulation were found to exhibit low levels of transient behavior relative to the empirical data. Even with these differences, the synthetic and empirical data in this study agree well in terms of driving speed and simulated fuel economy. The differences in transient behavior between simulated and empirical data suggest that larger stochastic contributions in traffic micro-simulation (relative to those present in the traffic micro-simulation tool used in this study) are required to fully capture the arbitrary elements of human driving. Interestingly, the lack of stochastic contributions from models of human drivers in this study did not result in a significant discrepancy between fuel economy simulations based on synthetic and empirical data; a finding with implications on the potential energy efficiency gains of automated vehicle technology.« less

Investigating Traffic Avoidance Maneuver Preferences of Unmanned Aircraft Operators

DTIC Science & Technology

2016-06-13

aircraft in the NAS under instrument flight rules ( IFR ), in radio communications with ATC, and with a traffic display highlighting traffic within 80...Lincoln Laboratory developed uncorrelated encounter model [13] for evaluation of a preliminary pilot model. The UAS was assumed to be on an IFR ...Vol. 59, No. 1, Human Factors and Ergonomics Society, Santa Monica, CA, 2015, pp. 45-49. [10] Rorie, R. C., Fern, L., and Shively R. J., “The impact

Performance of an Automated-Mixed-Traffic-Vehicle /AMTV/ System. [urban people mover

NASA Technical Reports Server (NTRS)

Peng, T. K. C.; Chon, K.

1978-01-01

This study analyzes the operation and evaluates the expected performance of a proposed automatic guideway transit system which uses low-speed Automated Mixed Traffic Vehicles (AMTV's). Vehicle scheduling and headway control policies are evaluated with a transit system simulation model. The effect of mixed-traffic interference on the average vehicle speed is examined with a vehicle-pedestrian interface model. Control parameters regulating vehicle speed are evaluated for safe stopping and passenger comfort.

Automatic 3D high-fidelity traffic interchange modeling using 2D road GIS data

NASA Astrophysics Data System (ADS)

Wang, Jie; Shen, Yuzhong

2011-03-01

3D road models are widely used in many computer applications such as racing games and driving simulations. However, almost all high-fidelity 3D road models were generated manually by professional artists at the expense of intensive labor. There are very few existing methods for automatically generating 3D high-fidelity road networks, especially for those existing in the real world. Real road network contains various elements such as road segments, road intersections and traffic interchanges. Among them, traffic interchanges present the most challenges to model due to their complexity and the lack of height information (vertical position) of traffic interchanges in existing road GIS data. This paper proposes a novel approach that can automatically produce 3D high-fidelity road network models, including traffic interchange models, from real 2D road GIS data that mainly contain road centerline information. The proposed method consists of several steps. The raw road GIS data are first preprocessed to extract road network topology, merge redundant links, and classify road types. Then overlapped points in the interchanges are detected and their elevations are determined based on a set of level estimation rules. Parametric representations of the road centerlines are then generated through link segmentation and fitting, and they have the advantages of arbitrary levels of detail with reduced memory usage. Finally a set of civil engineering rules for road design (e.g., cross slope, superelevation) are selected and used to generate realistic road surfaces. In addition to traffic interchange modeling, the proposed method also applies to other more general road elements. Preliminary results show that the proposed method is highly effective and useful in many applications.

Review of modelling air pollution from traffic at street-level - The state of the science.

PubMed

Forehead, H; Huynh, N

2018-06-13

Traffic emissions are a complex and variable cocktail of toxic chemicals. They are the major source of atmospheric pollution in the parts of cities where people live, commute and work. Reducing exposure requires information about the distribution and nature of emissions. Spatially and temporally detailed data are required, because both the rate of production and the composition of emissions vary significantly with time of day and with local changes in wind, traffic composition and flow. Increasing computer processing power means that models can accept highly detailed inputs of fleet, fuels and road networks. The state of the science models can simulate the behaviour and emissions of all the individual vehicles on a road network, with resolution of a second and tens of metres. The chemistry of the simulated emissions is also highly resolved, due to consideration of multiple engine processes, fuel evaporation and tyre wear. Good results can be achieved with both commercially available and open source models. The extent of a simulation is usually limited by processing capacity; the accuracy by the quality of traffic data. Recent studies have generated real time, detailed emissions data by using inputs from novel traffic sensing technologies and data from intelligent traffic systems (ITS). Increasingly, detailed pollution data is being combined with spatially resolved demographic or epidemiological data for targeted risk analyses. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

Traffic Flow Management Wrap-Up

NASA Technical Reports Server (NTRS)

Grabbe, Shon

2011-01-01

Traffic Flow Management involves the scheduling and routing of air traffic subject to airport and airspace capacity constraints, and the efficient use of available airspace. Significant challenges in this area include: (1) weather integration and forecasting, (2) accounting for user preferences in the Traffic Flow Management decision making process, and (3) understanding and mitigating the environmental impacts of air traffic on the environment. To address these challenges, researchers in the Traffic Flow Management area are developing modeling, simulation and optimization techniques to route and schedule air traffic flights and flows while accommodating user preferences, accounting for system uncertainties and considering the environmental impacts of aviation. This presentation will highlight some of the major challenges facing researchers in this domain, while also showcasing recent innovations designed to address these challenges.

Effects of the amount of feedback information on urban traffic with advanced traveler information system

NASA Astrophysics Data System (ADS)

Chen, Jie; Li, Ming; Jiang, Rui; Hu, Mao-Bin

2017-09-01

In a real traffic system, information feedback has already been proven to be a good way to alleviate traffic jams. However, due to the massive traffic information of real system, the procedure is often difficult in practice. In this paper, we study the effects of the amount of feedback information based on a cellular automaton model of urban traffic. What we found most interesting is that when providing the traffic information of a part of a road to travelers, the performance of the system will be better than that providing the road's full traffic information. From this basis, we can provide more effective routing strategy with less information. We demonstrate that only providing the traffic information of about first half road from upstream to downstream can maximize the traffic capacity of the system. We also give an explanation for these phenomena by studying the distribution pattern of vehicles and the detailed turning environment at the intersections. The effects of the traffic light period are also provided.

Air Traffic Management Research at NASA Ames

NASA Technical Reports Server (NTRS)

Davis, Thomas J.

2012-01-01

The Aviation Systems Division at the NASA Ames Research Center conducts leading edge research in air traffic management concepts and technologies. This overview will present concepts and simulation results for research in traffic flow management, safe and efficient airport surface operations, super density terminal area operations, separation assurance and system wide modeling and simulation. A brief review of the ongoing air traffic management technology demonstration (ATD-1) will also be presented. A panel discussion, with Mr. Davis serving as a panelist, on air traffic research will follow the briefing.

Near-road air pollutant concentrations of CO and PM 2.5: A comparison of MOBILE6.2/CALINE4 and generalized additive models

NASA Astrophysics Data System (ADS)

Zhang, Kai; Batterman, Stuart

2010-05-01

The contribution of vehicular traffic to air pollutant concentrations is often difficult to establish. This paper utilizes both time-series and simulation models to estimate vehicle contributions to pollutant levels near roadways. The time-series model used generalized additive models (GAMs) and fitted pollutant observations to traffic counts and meteorological variables. A one year period (2004) was analyzed on a seasonal basis using hourly measurements of carbon monoxide (CO) and particulate matter less than 2.5 μm in diameter (PM 2.5) monitored near a major highway in Detroit, Michigan, along with hourly traffic counts and local meteorological data. Traffic counts showed statistically significant and approximately linear relationships with CO concentrations in fall, and piecewise linear relationships in spring, summer and winter. The same period was simulated using emission and dispersion models (Motor Vehicle Emissions Factor Model/MOBILE6.2; California Line Source Dispersion Model/CALINE4). CO emissions derived from the GAM were similar, on average, to those estimated by MOBILE6.2. The same analyses for PM 2.5 showed that GAM emission estimates were much higher (by 4-5 times) than the dispersion model results, and that the traffic-PM 2.5 relationship varied seasonally. This analysis suggests that the simulation model performed reasonably well for CO, but it significantly underestimated PM 2.5 concentrations, a likely result of underestimating PM 2.5 emission factors. Comparisons between statistical and simulation models can help identify model deficiencies and improve estimates of vehicle emissions and near-road air quality.

Comparative analysis of zonal systems for macro-level crash modeling.

PubMed

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

2017-06-01

Macro-level traffic safety analysis has been undertaken at different spatial configurations. However, clear guidelines for the appropriate zonal system selection for safety analysis are unavailable. In this study, a comparative analysis was conducted to determine the optimal zonal system for macroscopic crash modeling considering census tracts (CTs), state-wide traffic analysis zones (STAZs), and a newly developed traffic-related zone system labeled traffic analysis districts (TADs). Poisson lognormal models for three crash types (i.e., total, severe, and non-motorized mode crashes) are developed based on the three zonal systems without and with consideration of spatial autocorrelation. The study proposes a method to compare the modeling performance of the three types of geographic units at different spatial configurations through a grid based framework. Specifically, the study region is partitioned to grids of various sizes and the model prediction accuracy of the various macro models is considered within these grids of various sizes. These model comparison results for all crash types indicated that the models based on TADs consistently offer a better performance compared to the others. Besides, the models considering spatial autocorrelation outperform the ones that do not consider it. Based on the modeling results and motivation for developing the different zonal systems, it is recommended using CTs for socio-demographic data collection, employing TAZs for transportation demand forecasting, and adopting TADs for transportation safety planning. The findings from this study can help practitioners select appropriate zonal systems for traffic crash modeling, which leads to develop more efficient policies to enhance transportation safety. Copyright © 2017 Elsevier Ltd and National Safety Council. All rights reserved.

An empirically grounded agent based model for modeling directs, conflict detection and resolution operations in air traffic management.

PubMed

Bongiorno, Christian; Miccichè, Salvatore; Mantegna, Rosario N

2017-01-01

We present an agent based model of the Air Traffic Management socio-technical complex system aiming at modeling the interactions between aircraft and air traffic controllers at a tactical level. The core of the model is given by the conflict detection and resolution module and by the directs module. Directs are flight shortcuts that are given by air controllers to speed up the passage of an aircraft within a certain airspace and therefore to facilitate airline operations. Conflicts between flight trajectories can occur for two main reasons: either the planning of the flight trajectory was not sufficiently detailed to rule out all potential conflicts or unforeseen events during the flight require modifications of the flight plan that can conflict with other flight trajectories. Our model performs a local conflict detection and resolution procedure. Once a flight trajectory has been made conflict-free, the model searches for possible improvements of the system efficiency by issuing directs. We give an example of model calibration based on real data. We then provide an illustration of the capability of our model in generating scenario simulations able to give insights about the air traffic management system. We show that the calibrated model is able to reproduce the existence of a geographical localization of air traffic controllers' operations. Finally, we use the model to investigate the relationship between directs and conflict resolutions (i) in the presence of perfect forecast ability of controllers, and (ii) in the presence of some degree of uncertainty in flight trajectory forecast.

An empirically grounded agent based model for modeling directs, conflict detection and resolution operations in air traffic management

PubMed Central

Bongiorno, Christian; Mantegna, Rosario N.

2017-01-01

We present an agent based model of the Air Traffic Management socio-technical complex system aiming at modeling the interactions between aircraft and air traffic controllers at a tactical level. The core of the model is given by the conflict detection and resolution module and by the directs module. Directs are flight shortcuts that are given by air controllers to speed up the passage of an aircraft within a certain airspace and therefore to facilitate airline operations. Conflicts between flight trajectories can occur for two main reasons: either the planning of the flight trajectory was not sufficiently detailed to rule out all potential conflicts or unforeseen events during the flight require modifications of the flight plan that can conflict with other flight trajectories. Our model performs a local conflict detection and resolution procedure. Once a flight trajectory has been made conflict-free, the model searches for possible improvements of the system efficiency by issuing directs. We give an example of model calibration based on real data. We then provide an illustration of the capability of our model in generating scenario simulations able to give insights about the air traffic management system. We show that the calibrated model is able to reproduce the existence of a geographical localization of air traffic controllers’ operations. Finally, we use the model to investigate the relationship between directs and conflict resolutions (i) in the presence of perfect forecast ability of controllers, and (ii) in the presence of some degree of uncertainty in flight trajectory forecast. PMID:28419160

Traffic modeling of transit oriented development : evaluation of transit friendly strategies and innovative intersection designs in West Valley City, UT.

DOT National Transportation Integrated Search

2014-07-01

Street networks designed to support Transit Oriented Development (TOD) increase accessibility for non-motorized traffic. However, the implications of TOD supportive networks for still dominant vehicular : traffic are rarely addressed. Due to this lac...

40 CFR 52.1164 - Localized high concentrations-carbon monoxide.

Code of Federal Regulations, 2010 CFR

2010-07-01

... meteorological modeling, traffic flow monitoring, air quality monitoring and other measures necessary to... reviewing all available traffic data, physical site data and air quality and meteorological data for all... containing measures to regulate traffic and parking so as to reduce carbon monoxide emissions to achieve air...

40 CFR 52.1164 - Localized high concentrations-carbon monoxide.

Code of Federal Regulations, 2014 CFR

2014-07-01

... meteorological modeling, traffic flow monitoring, air quality monitoring and other measures necessary to... reviewing all available traffic data, physical site data and air quality and meteorological data for all... containing measures to regulate traffic and parking so as to reduce carbon monoxide emissions to achieve air...

40 CFR 52.1164 - Localized high concentrations-carbon monoxide.

Code of Federal Regulations, 2013 CFR

2013-07-01

... meteorological modeling, traffic flow monitoring, air quality monitoring and other measures necessary to... reviewing all available traffic data, physical site data and air quality and meteorological data for all... containing measures to regulate traffic and parking so as to reduce carbon monoxide emissions to achieve air...

40 CFR 52.1164 - Localized high concentrations-carbon monoxide.

Code of Federal Regulations, 2011 CFR

2011-07-01

... meteorological modeling, traffic flow monitoring, air quality monitoring and other measures necessary to... reviewing all available traffic data, physical site data and air quality and meteorological data for all... containing measures to regulate traffic and parking so as to reduce carbon monoxide emissions to achieve air...

40 CFR 52.1164 - Localized high concentrations-carbon monoxide.

Code of Federal Regulations, 2012 CFR

2012-07-01

... meteorological modeling, traffic flow monitoring, air quality monitoring and other measures necessary to... reviewing all available traffic data, physical site data and air quality and meteorological data for all... containing measures to regulate traffic and parking so as to reduce carbon monoxide emissions to achieve air...

Using mobile probes to inform and measure the effectiveness of macroscopic traffic control strategies on urban networks.

DOT National Transportation Integrated Search

2015-06-01

Urban traffic congestion is a problem that plagues many cities in the United States. Testing strategies to alleviate this : congestion is especially challenging due to the difficulty of modeling complex urban traffic networks. However, recent work ha...

A microcomputer based traffic evacuation modeling system for emergency planning application

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

Rathi, A.K.

1995-12-31

The US Army stockpiles unitary chemical weapons, both as bulk chemicals and as munitions, at eight major sites in the United States. The continued storage and disposal of the chemical stockpile has the potential for accidental releases of toxic gases that could escape the installation boundaries and pose a threat to the civilian population in the vicinity. 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 evacuatemore » 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 this 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

A microscopic lane changing process model for multilane traffic

NASA Astrophysics Data System (ADS)

Lv, Wei; Song, Wei-guo; Liu, Xiao-dong; Ma, Jian

2013-03-01

In previous simulations lane-changing behavior is usually assumed as an instantaneous action. However, in real traffic, lane changing is a continuing process which can seriously affect the following cars. In this paper, a microscopic lane-changing process (LCP) model is clearly described. A new idea of simplifying the lane-changing process to the car-following framework is presented by controlling fictitious cars. To verify the model, the results of flow, lane-changing frequency, and single-car velocity are extracted from experimental observations and are compared with corresponding simulation. It is found that the LCP model agrees well with actual traffic flow and lane-changing behaviors may induce a 12%-18% reduction of traffic flow. The results also reflect that most of the drivers on the two roads in a city are conservative but not aggressive to change lanes. Investigation of lane-changing frequency shows that the largest lane-changing frequency occurs at a medium density range from 15 vehs km lane to 35 vehs km lane. It also implies that the lane-changing process might strengthen velocity variation at medium density and weaken velocity variation at high density. It is hoped that the idea of this study may be helpful to promote the modeling and simulation study of traffic flow.

Research on Influence and Prediction Model of Urban Traffic Link Tunnel curvature on Fire Temperature Based on Pyrosim--SPSS Multiple Regression Analysis

NASA Astrophysics Data System (ADS)

Li, Xiao Ju; Yao, Kun; Dai, Jun Yu; Song, Yun Long

2018-05-01

The underground space, also known as the “fourth dimension” of the city, reflects the efficient use of urban development intensive. Urban traffic link tunnel is a typical underground limited-length space. Due to the geographical location, the special structure of space and the curvature of the tunnel, high-temperature smoke can easily form the phenomenon of “smoke turning” and the fire risk is extremely high. This paper takes an urban traffic link tunnel as an example to focus on the relationship between curvature and the temperature near the fire source, and use the pyrosim built different curvature fire model to analyze the influence of curvature on the temperature of the fire, then using SPSS Multivariate regression analysis simulate curvature of the tunnel and fire temperature data. Finally, a prediction model of urban traffic link tunnel curvature on fire temperature was proposed. The regression model analysis and test show that the curvature is negatively correlated with the tunnel temperature. This model is feasible and can provide a theoretical reference for the urban traffic link tunnel fire protection design and the preparation of the evacuation plan. And also, it provides some reference for other related curved tunnel curvature design and smoke control measures.

Analysis of traffic congestion induced by the work zone

NASA Astrophysics Data System (ADS)

Fei, L.; Zhu, H. B.; Han, X. L.

2016-05-01

Based on the cellular automata model, a meticulous two-lane cellular automata model is proposed, in which the driving behavior difference and the difference of vehicles' accelerations between the moving state and the starting state are taken into account. Furthermore the vehicles' motion is refined by using the small cell of one meter long. Then accompanied by coming up with a traffic management measure, a two-lane highway traffic model containing a work zone is presented, in which the road is divided into normal area, merging area and work zone. The vehicles in different areas move forward according to different lane changing rules and position updating rules. After simulation it is found that when the density is small the cluster length in front of the work zone increases with the decrease of the merging probability. Then the suitable merging length and the appropriate speed limit value are recommended. The simulation result in the form of the speed-flow diagram is in good agreement with the empirical data. It indicates that the presented model is efficient and can partially reflect the real traffic. The results may be meaningful for traffic optimization and road construction management.

Statistical physics of vehicular traffic and some related systems

NASA Astrophysics Data System (ADS)

Chowdhury, Debashish; Santen, Ludger; Schadschneider, Andreas

2000-05-01

In the so-called “microscopic” models of vehicular traffic, attention is paid explicitly to each individual vehicle each of which is represented by a “particle”; the nature of the “interactions” among these particles is determined by the way the vehicles influence each others’ movement. Therefore, vehicular traffic, modeled as a system of interacting “particles” driven far from equilibrium, offers the possibility to study various fundamental aspects of truly nonequilibrium systems which are of current interest in statistical physics. Analytical as well as numerical techniques of statistical physics are being used to study these models to understand rich variety of physical phenomena exhibited by vehicular traffic. Some of these phenomena, observed in vehicular traffic under different circumstances, include transitions from one dynamical phase to another, criticality and self-organized criticality, metastability and hysteresis, phase-segregation, etc. In this critical review, written from the perspective of statistical physics, we explain the guiding principles behind all the main theoretical approaches. But we present detailed discussions on the results obtained mainly from the so-called “particle-hopping” models, particularly emphasizing those which have been formulated in recent years using the language of cellular automata.

Cross-layer model design in wireless ad hoc networks for the Internet of Things.

PubMed

Yang, Xin; Wang, Ling; Xie, Jian; Zhang, Zhaolin

2018-01-01

Wireless ad hoc networks can experience extreme fluctuations in transmission traffic in the Internet of Things, which is widely used today. Currently, the most crucial issues requiring attention for wireless ad hoc networks are making the best use of low traffic periods, reducing congestion during high traffic periods, and improving transmission performance. To solve these problems, the present paper proposes a novel cross-layer transmission model based on decentralized coded caching in the physical layer and a content division multiplexing scheme in the media access control layer. Simulation results demonstrate that the proposed model effectively addresses these issues by substantially increasing the throughput and successful transmission rate compared to existing protocols without a negative influence on delay, particularly for large scale networks under conditions of highly contrasting high and low traffic periods.

Cross-layer model design in wireless ad hoc networks for the Internet of Things

PubMed Central

Wang, Ling; Xie, Jian; Zhang, Zhaolin

2018-01-01

Wireless ad hoc networks can experience extreme fluctuations in transmission traffic in the Internet of Things, which is widely used today. Currently, the most crucial issues requiring attention for wireless ad hoc networks are making the best use of low traffic periods, reducing congestion during high traffic periods, and improving transmission performance. To solve these problems, the present paper proposes a novel cross-layer transmission model based on decentralized coded caching in the physical layer and a content division multiplexing scheme in the media access control layer. Simulation results demonstrate that the proposed model effectively addresses these issues by substantially increasing the throughput and successful transmission rate compared to existing protocols without a negative influence on delay, particularly for large scale networks under conditions of highly contrasting high and low traffic periods. PMID:29734355

A cellular automation model accounting for bicycle's group behavior

NASA Astrophysics Data System (ADS)

Tang, Tie-Qiao; Rui, Ying-Xu; Zhang, Jian; Shang, Hua-Yan

2018-02-01

Recently, bicycle has become an important traffic tool in China, again. Due to the merits of bicycle, the group behavior widely exists in urban traffic system. However, little effort has been made to explore the impacts of the group behavior on bicycle flow. In this paper, we propose a CA (cellular automaton) model with group behavior to explore the complex traffic phenomena caused by shoulder group behavior and following group behavior on an open road. The numerical results illustrate that the proposed model can qualitatively describe the impacts of the two kinds of group behaviors on bicycle flow and that the effects are related to the mode and size of group behaviors. The results can help us to better understand the impacts of the bicycle's group behaviors on urban traffic system and effectively control the bicycle's group behavior.

Airfreight forecasting methodology and results

NASA Technical Reports Server (NTRS)

1978-01-01

A series of econometric behavioral equations was developed to explain and forecast the evolution of airfreight traffic demand for the total U.S. domestic airfreight system, the total U.S. international airfreight system, and the total scheduled international cargo traffic carried by the top 44 foreign airlines. The basic explanatory variables used in these macromodels were the real gross national products of the countries involved and a measure of relative transportation costs. The results of the econometric analysis reveal that the models explain more than 99 percent of the historical evolution of freight traffic. The long term traffic forecasts generated with these models are based on scenarios of the likely economic outlook in the United States and 31 major foreign countries.

Meeting the customer's needs for mobility and safety during construction and maintenance operations : model work zone traffic management program and self evaluation guide

DOT National Transportation Integrated Search

1998-09-01

This model program was developed by combining traffic management concepts reported in research studies and papers with the effective techniques currently being used by States to minimize motorist delays and enhance work zone safety. This model is pre...

Traffic model for advanced satellite designs and experiments for ISDN services

NASA Technical Reports Server (NTRS)

Pepin, Gerard R.; Hager, E. Paul

1991-01-01

The data base structure and fields for categorizing and storing Integrated Services Digital Network (ISDN) user characteristics is outlined. This traffic model data base will be used to exercise models of the ISDN Advanced Communication Satellite to determine design parameters and performance for the NASA Satellite Communications Applications Research (SCAR) Program.

A new bus lane on urban expressway with no-bay bus stop

NASA Astrophysics Data System (ADS)

Tian, Zhao; Jia, Limin

2016-01-01

The sharp increase in residents and vehicles causes heavy traffic pressure in many cities. To ease traffic congestion, it has been the common sense that we should develop public transit system. The priority of the bus appears particularly necessary with the rapid development of the public transport system. The bus lane is an important embodiment of the bus priority. Focusing on the problem of the unreasonable dedicated bus lane (DBL) under the lower ratio of buses, this paper proposed a new bus lane with limited physical length. And this bus lane can reduce the lane-changing conflict caused by the buses and cars running on roads without bus lanes. Based on the cellular automata (CA) traffic flow model and the lane-changing behavior of the vehicle including the optional lane-changing and the mandatory lane-changing, a three-lane traffic model with an isolated no-bay bus stop is proposed. The ordinary three-lane traffic without a bus lane and the cases of traffic with a DBL or the proposed bus lane are simulated, and the comparisons in the form of the fundamental diagrams are made among them. It is shown that the no-bay bus stop can act as a bottleneck on the traffic flow because of the mandatory lane-changing behavior. Under a certain ratio of the bus number to the total vehicles number, (1) the traffic with the proposed bus lane has less lane-changing conflict and can provide higher traffic capacity than the ordinary traffic without a bus lane, (2) compared with the DBL, the proposed bus lane is advantageous in easing congestion on the ordinary lanes when the traffic flow is high and can avoid unreasonable allocation of the road resources.

CATS-based Air Traffic Controller Agents

NASA Technical Reports Server (NTRS)

Callantine, Todd J.

2002-01-01

This report describes intelligent agents that function as air traffic controllers. Each agent controls traffic in a single sector in real time; agents controlling traffic in adjoining sectors can coordinate to manage an arrival flow across a given meter fix. The purpose of this research is threefold. First, it seeks to study the design of agents for controlling complex systems. In particular, it investigates agent planning and reactive control functionality in a dynamic environment in which a variety perceptual and decision making skills play a central role. It examines how heuristic rules can be applied to model planning and decision making skills, rather than attempting to apply optimization methods. Thus, the research attempts to develop intelligent agents that provide an approximation of human air traffic controller behavior that, while not based on an explicit cognitive model, does produce task performance consistent with the way human air traffic controllers operate. Second, this research sought to extend previous research on using the Crew Activity Tracking System (CATS) as the basis for intelligent agents. The agents use a high-level model of air traffic controller activities to structure the control task. To execute an activity in the CATS model, according to the current task context, the agents reference a 'skill library' and 'control rules' that in turn execute the pattern recognition, planning, and decision-making required to perform the activity. Applying the skills enables the agents to modify their representation of the current control situation (i.e., the 'flick' or 'picture'). The updated representation supports the next activity in a cycle of action that, taken as a whole, simulates air traffic controller behavior. A third, practical motivation for this research is to use intelligent agents to support evaluation of new air traffic control (ATC) methods to support new Air Traffic Management (ATM) concepts. Current approaches that use large, human-in-the-loop simulations are unquestionably valuable for this purpose, but pose considerable logistical, fiscal, and experimental control problems. First, data analysis is extremely complicated, owing simply to the large number of participants and data sources in such simulations. In addition, experienced human air traffic controllers working adjacent sectors tend to flexibly adapt to the evolving control problem - potentially shifting to other strategies than those under investigation. In addition, their performance is tightly coupled to the control interface, which in the development phase may support some concepts and supporting strategies better than others. A simple shift in strategy by one controller can change the character of a particular traffic scenario dramatically, which makes experimental comparison of ATC performance under different traffic scenarios difficult. Training a given team of controllers on operations under a new ATM concept for a sufficient period of time could avert such difficulties, but instituting an adequate training program is expensive and logistically difficult.

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.

Predicting Average Vehicle Speed in Two Lane Highways Considering Weather Condition and Traffic Characteristics

NASA Astrophysics Data System (ADS)

Mirbaha, Babak; Saffarzadeh, Mahmoud; AmirHossein Beheshty, Seyed; Aniran, MirMoosa; Yazdani, Mirbahador; Shirini, Bahram

2017-10-01

Analysis of vehicle speed with different weather condition and traffic characteristics is very effective in traffic planning. Since the weather condition and traffic characteristics vary every day, the prediction of average speed can be useful in traffic management plans. In this study, traffic and weather data for a two-lane highway located in Northwest of Iran were selected for analysis. After merging traffic and weather data, the linear regression model was calibrated for speed prediction using STATA12.1 Statistical and Data Analysis software. Variables like vehicle flow, percentage of heavy vehicles, vehicle flow in opposing lane, percentage of heavy vehicles in opposing lane, rainfall (mm), snowfall and maximum daily wind speed more than 13m/s were found to be significant variables in the model. Results showed that variables of vehicle flow and heavy vehicle percent acquired the positive coefficient that shows, by increasing these variables the average vehicle speed in every weather condition will also increase. Vehicle flow in opposing lane, percentage of heavy vehicle in opposing lane, rainfall amount (mm), snowfall and maximum daily wind speed more than 13m/s acquired the negative coefficient that shows by increasing these variables, the average vehicle speed will decrease.

Understanding widely scattered traffic flows, the capacity drop, and platoons as effects of variance-driven time gaps

NASA Astrophysics Data System (ADS)

Treiber, Martin; Kesting, Arne; Helbing, Dirk

2006-07-01

We investigate the adaptation of the time headways in car-following models as a function of the local velocity variance, which is a measure of the inhomogeneity of traffic flow. We apply this mechanism to several car-following models and simulate traffic breakdowns in open systems with an on-ramp as bottleneck and in a closed ring road. Single-vehicle data and one-minute aggregated data generated by several virtual detectors show a semiquantitative agreement with microscopic and flow-density data from the Dutch freeway A9. This includes the observed distributions of the net time headways for free and congested traffic, the velocity variance as a function of density, and the fundamental diagram. The modal value of the time headway distribution is shifted by a factor of about 2 under congested conditions. Macroscopically, this corresponds to the capacity drop at the transition from free to congested traffic. The simulated fundamental diagram shows free, synchronized, and jammed traffic, and a wide scattering in the congested traffic regime. We explain this by a self-organized variance-driven process that leads to the spontaneous formation and decay of long-lived platoons even for a deterministic dynamics on a single lane.

Marine traffic model based on cellular automaton: Considering the change of the ship's velocity under the influence of the weather and sea

NASA Astrophysics Data System (ADS)

Qi, Le; Zheng, Zhongyi; Gang, Longhui

2017-10-01

It was found that the ships' velocity change, which is impacted by the weather and sea, e.g., wind, sea wave, sea current, tide, etc., is significant and must be considered in the marine traffic model. Therefore, a new marine traffic model based on cellular automaton (CA) was proposed in this paper. The characteristics of the ship's velocity change are taken into account in the model. First, the acceleration of a ship was divided into two components: regular component and random component. Second, the mathematical functions and statistical distribution parameters of the two components were confirmed by spectral analysis, curve fitting and auto-correlation analysis methods. Third, by combining the two components, the acceleration was regenerated in the update rules for ships' movement. To test the performance of the model, the ship traffic flows in the Dover Strait, the Changshan Channel and the Qiongzhou Strait were studied and simulated. The results show that the characteristics of ships' velocities in the simulations are consistent with the measured data by Automatic Identification System (AIS). Although the characteristics of the traffic flow in different areas are different, the velocities of ships can be simulated correctly. It proves that the velocities of ships under the influence of weather and sea can be simulated successfully using the proposed model.

Navier-Stokes-like equations for traffic flow.

PubMed

Velasco, R M; Marques, W

2005-10-01

The macroscopic traffic flow equations derived from the reduced Paveri-Fontana equation are closed starting with the maximization of the informational entropy. The homogeneous steady state taken as a reference is obtained for a specific model of the desired velocity and a kind of Chapman-Enskog method is developed to calculate the traffic pressure at the Navier-Stokes level. Numerical solution of the macroscopic traffic equations is obtained and its characteristics are analyzed.

An Adaptive Fuzzy-Logic Traffic Control System in Conditions of Saturated Transport Stream

PubMed Central

Marakhimov, A. R.; Igamberdiev, H. Z.; Umarov, Sh. X.

2016-01-01

This paper considers the problem of building adaptive fuzzy-logic traffic control systems (AFLTCS) to deal with information fuzziness and uncertainty in case of heavy traffic streams. Methods of formal description of traffic control on the crossroads based on fuzzy sets and fuzzy logic are proposed. This paper also provides efficient algorithms for implementing AFLTCS and develops the appropriate simulation models to test the efficiency of suggested approach. PMID:27517081

Intelligent driving in traffic systems with partial lane discipline

NASA Astrophysics Data System (ADS)

Assadi, Hamid; Emmerich, Heike

2013-04-01

It is a most common notion in traffic theory that driving in lanes and keeping lane changes to a minimum leads to smooth and laminar traffic flow, and hence to increased traffic capacity. On the other hand, there exist persistent vehicular traffic systems that are characterised by habitual disregarding of lane markings, and partial or complete loss of laminar traffic flow. Here, we explore the stability of such systems through a microscopic traffic flow model, where the degree of lane-discipline is taken as a variable, represented by the fraction of drivers that disregard lane markings completely. The results show that lane-free traffic may win over completely ordered traffic at high densities, and that partially ordered traffic leads to the poorest overall flow, while not considering the crash probability. Partial order in a lane-free system is similar to partial disorder in a lane-disciplined system in that both lead to decreased traffic capacity. This could explain the reason why standard enforcement methods, which rely on continuous increase of order, often fail to incur order to lane-free traffic systems. The results also provide an insight into the cooperative phenomena in open systems with self-driven particles.

Estimate of main local sources to ambient ultrafine particle number concentrations in an urban area

NASA Astrophysics Data System (ADS)

Rahman, Md Mahmudur; Mazaheri, Mandana; Clifford, Sam; Morawska, Lidia

2017-09-01

Quantifying and apportioning the contribution of a range of sources to ultrafine particles (UFPs, D < 100 nm) is a challenge due to the complex nature of the urban environments. Although vehicular emissions have long been considered one of the major sources of ultrafine particles in urban areas, the contribution of other major urban sources is not yet fully understood. This paper aims to determine and quantify the contribution of local ground traffic, nucleated particle (NP) formation and distant non-traffic (e.g. airport, oil refineries, and seaport) sources to the total ambient particle number concentration (PNC) in a busy, inner-city area in Brisbane, Australia using Bayesian statistical modelling and other exploratory tools. The Bayesian model was trained on the PNC data on days where NP formations were known to have not occurred, hourly traffic counts, solar radiation data, and smooth daily trend. The model was applied to apportion and quantify the contribution of NP formations and local traffic and non-traffic sources to UFPs. The data analysis incorporated long-term measured time-series of total PNC (D ≥ 6 nm), particle number size distributions (PSD, D = 8 to 400 nm), PM2.5, PM10, NOx, CO, meteorological parameters and traffic counts at a stationary monitoring site. The developed Bayesian model showed reliable predictive performances in quantifying the contribution of NP formation events to UFPs (up to 4 × 104 particles cm- 3), with a significant day to day variability. The model identified potential NP formation and no-formations days based on PNC data and quantified the sources contribution to UFPs. Exploratory statistical analyses show that total mean PNC during the middle of the day was up to 32% higher than during peak morning and evening traffic periods, which were associated with NP formation events. The majority of UFPs measured during the peak traffic and NP formation periods were between 30-100 nm and smaller than 30 nm, respectively. To date, this is the first application of Bayesian model to apportion different sources contribution to UFPs, and therefore the importance of this study is not only in its modelling outcomes but in demonstrating the applicability and advantages of this statistical approach to air pollution studies.

Prediction of PM 10 concentrations at urban traffic intersections using semi-empirical box modelling with instantaneous velocity and acceleration

NASA Astrophysics Data System (ADS)

He, Hong-di; Lu, Wei-Zhen; Xue, Yu

2009-12-01

At urban traffic intersections, vehicles frequently stop with idling engines during the red-light period and speed up rapidly during the green-light period. The changes of driving patterns (i.e., idle, acceleration, deceleration and cruising patterns) generally produce uncertain emission. Additionally, the movement of pedestrians and the influence of wind further result in the random dispersion of pollutants. It is, therefore, too complex to simulate the effects of such dynamics on the resulting emission using conventional deterministic causal models. For this reason, a modified semi-empirical box model for predicting the PM 10 concentrations on roadsides is proposed in this paper. The model constitutes three parts, i.e., traffic, emission and dispersion components. The traffic component is developed using a generalized force traffic model to obtain the instantaneous velocity and acceleration when vehicles move through intersections. Hence the distribution of vehicle emission in street canyon during the green-light period is calculated. Then the dispersion component is investigated using a semi-empirical box model combining average wind speed, box height and background concentrations. With these considerations, the proposed model is applied and evaluated using measured data at a busy traffic intersection in Mong Kok, Hong Kong. In order to test the performance of the model, two situations, i.e., the data sets within a sunny day and between two sunny days, were selected to examine the model performance. The predicted values are generally well coincident with the observed data during different time slots except several values are overestimated or underestimated. Moreover, two types of vehicles, i.e., buses and petrol cars, are separately taken into account in the study. Buses are verified to contribute most to the emission in street canyons, which may be useful in evaluating the impact of vehicle emissions on the ambient air quality when there is a significant change in a specific vehicular population.

An optimal general type-2 fuzzy controller for Urban Traffic Network.

PubMed

Khooban, Mohammad Hassan; Vafamand, Navid; Liaghat, Alireza; Dragicevic, Tomislav

2017-01-01

Urban traffic network model is illustrated by state-charts and object-diagram. However, they have limitations to show the behavioral perspective of the Traffic Information flow. Consequently, a state space model is used to calculate the half-value waiting time of vehicles. In this study, a combination of the general type-2 fuzzy logic sets and the Modified Backtracking Search Algorithm (MBSA) techniques are used in order to control the traffic signal scheduling and phase succession so as to guarantee a smooth flow of traffic with the least wait times and average queue length. The parameters of input and output membership functions are optimized simultaneously by the novel heuristic algorithm MBSA. A comparison is made between the achieved results with those of optimal and conventional type-1 fuzzy logic controllers. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Use of Cusp Catastrophe for Risk Analysis of Navigational Environment: A Case Study of Three Gorges Reservoir Area

PubMed Central

Hao, Guozhu

2016-01-01

A water traffic system is a huge, nonlinear, complex system, and its stability is affected by various factors. Water traffic accidents can be considered to be a kind of mutation of a water traffic system caused by the coupling of multiple navigational environment factors. In this study, the catastrophe theory, principal component analysis (PCA), and multivariate statistics are integrated to establish a situation recognition model for a navigational environment with the aim of performing a quantitative analysis of the situation of this environment via the extraction and classification of its key influencing factors; in this model, the natural environment and traffic environment are considered to be two control variables. The Three Gorges Reservoir area of the Yangtze River is considered as an example, and six critical factors, i.e., the visibility, wind, current velocity, route intersection, channel dimension, and traffic flow, are classified into two principal components: the natural environment and traffic environment. These two components are assumed to have the greatest influence on the navigation risk. Then, the cusp catastrophe model is employed to identify the safety situation of the regional navigational environment in the Three Gorges Reservoir area. The simulation results indicate that the situation of the navigational environment of this area is gradually worsening from downstream to upstream. PMID:27391057

The effects of velocity difference changes with memory on the dynamics characteristics and fuel economy of traffic flow

NASA Astrophysics Data System (ADS)

Yu, Shaowei; Zhao, Xiangmo; Xu, Zhigang; Zhang, Licheng

2016-11-01

To evaluate the effects of velocity difference changes with memory in the intelligent transportation environment on the dynamics and fuel consumptions of traffic flow, we first investigate the linkage between velocity difference changes with memory and car-following behaviors with the measured data in cities, and then propose an improved cooperative car-following model considering multiple velocity difference changes with memory in the cooperative adaptive cruise control strategy, finally carry out several numerical simulations under the periodic boundary condition and at signalized intersections to explore how velocity difference changes with memory affect car's velocity, velocity fluctuation, acceleration and fuel consumptions in the intelligent transportation environment. The results show that velocity difference changes with memory have obvious effects on car-following behaviors, that the improved cooperative car-following model can describe the phase transition of traffic flow and estimate the evolution of traffic congestion, that the stability and fuel economy of traffic flow simulated by the improved car-following model with velocity difference changes with memory is obviously superior to those without velocity difference changes, and that taking velocity difference changes with memory into account in designing the advanced adaptive cruise control strategy can significantly improve the stability and fuel economy of traffic flow.

Capacity Estimation Model for Signalized Intersections under the Impact of Access Point

PubMed Central

Zhao, Jing; Li, Peng; Zhou, Xizhao

2016-01-01

Highway Capacity Manual 2010 provides various factors to adjust the base saturation flow rate for the capacity analysis of signalized intersections. No factors, however, is considered for the potential change of signalized intersections capacity caused by the access point closeing to the signalized intersection. This paper presented a theoretical model to estimate the lane group capacity at signalized intersections with the consideration of the effects of access points. Two scenarios of access point locations, upstream or downstream of the signalized intersection, and impacts of six types of access traffic flow are taken into account. The proposed capacity model was validated based on VISSIM simulation. Results of extensive numerical analysis reveal the substantial impact of access point on the capacity, which has an inverse correlation with both the number of major street lanes and the distance between the intersection and access point. Moreover, among the six types of access traffic flows, the access traffic flow 1 (right-turning traffic from major street), flow 4 (left-turning traffic from access point), and flow 5 (left-turning traffic from major street) cause a more significant effect on lane group capacity than others. Some guidance on the mitigation of the negative effect is provided for practitioners. PMID:26726998

Use of Cusp Catastrophe for Risk Analysis of Navigational Environment: A Case Study of Three Gorges Reservoir Area.

PubMed

Jiang, Dan; Hao, Guozhu; Huang, Liwen; Zhang, Dan

2016-01-01

A water traffic system is a huge, nonlinear, complex system, and its stability is affected by various factors. Water traffic accidents can be considered to be a kind of mutation of a water traffic system caused by the coupling of multiple navigational environment factors. In this study, the catastrophe theory, principal component analysis (PCA), and multivariate statistics are integrated to establish a situation recognition model for a navigational environment with the aim of performing a quantitative analysis of the situation of this environment via the extraction and classification of its key influencing factors; in this model, the natural environment and traffic environment are considered to be two control variables. The Three Gorges Reservoir area of the Yangtze River is considered as an example, and six critical factors, i.e., the visibility, wind, current velocity, route intersection, channel dimension, and traffic flow, are classified into two principal components: the natural environment and traffic environment. These two components are assumed to have the greatest influence on the navigation risk. Then, the cusp catastrophe model is employed to identify the safety situation of the regional navigational environment in the Three Gorges Reservoir area. The simulation results indicate that the situation of the navigational environment of this area is gradually worsening from downstream to upstream.

An improved car-following model with multiple preceding cars' velocity fluctuation feedback

NASA Astrophysics Data System (ADS)

Guo, Lantian; Zhao, Xiangmo; Yu, Shaowei; Li, Xiuhai; Shi, Zhongke

2017-04-01

In order to explore and evaluate the effects of velocity variation trend of multiple preceding cars used in the Cooperative Adaptive Cruise Control (CACC) strategy on the dynamic characteristic, fuel economy and emission of the corresponding traffic flow, we conduct a study as follows: firstly, with the real-time car-following (CF) data, the close relationship between multiple preceding cars' velocity fluctuation feedback and the host car's behaviors is explored, the evaluation results clearly show that multiple preceding cars' velocity fluctuation with different time window-width are highly correlated to the host car's acceleration/deceleration. Then, a microscopic traffic flow model is proposed to evaluate the effects of multiple preceding cars' velocity fluctuation feedback in the CACC strategy on the traffic flow evolution process. Finally, numerical simulations on fuel economy and exhaust emission of the traffic flow are also implemented by utilizing VT-micro model. Simulation results prove that considering multiple preceding cars' velocity fluctuation feedback in the control strategy of the CACC system can improve roadway traffic mobility, fuel economy and exhaust emission performance.

Building a Multivariable Linear Regression Model of On-road Traffic for Creation of High Resolution Emission Inventories

NASA Astrophysics Data System (ADS)

Powell, James Eckhardt

Emissions inventories are an important tool, often built by governments, and used to manage emissions. To build an inventory of urban CO2 emissions and other fossil fuel combustion products in the urban atmosphere, an inventory of on-road traffic is required. In particular, a high resolution inventory is necessary to capture the local characteristics of transport emissions. These emissions vary widely due to the local nature of the fleet, fuel, and roads. Here we show a new model of ADT for the Portland, OR metropolitan region. The backbone is traffic counter recordings made by the Portland Bureau of Transportation at 7,767 sites over 21 years (1986-2006), augmented with PORTAL (The Portland Regional Transportation Archive Listing) freeway traffic count data. We constructed a regression model to fill in traffic network gaps using GIS data such as road class and population density. An EPA-supplied emissions factor was used to estimate transportation CO2 emissions, which is compared to several other estimates for the city's CO2 footprint.

Studies of air traffic forecasts, airspace load and the effect of ADS-B via satellites on flight times

NASA Astrophysics Data System (ADS)

Zhong, Z. W.; Ridhwan Salleh, Saiful; Chow, W. X.; Ong, Z. M.

2016-10-01

Air traffic forecasting is important as it helps stakeholders to plan their budgets and facilities. Thus, three most commonly used forecasting models were compared to see which model suited the air passenger traffic the best. General forecasting equations were also created to forecast the passenger traffic. The equations could forecast around 6.0% growth from 2015 onwards. Another study sought to provide an initial work for determining a theoretical airspace load with relevant calculations. The air traffic was simulated to investigate the current airspace load. Logical and reasonable results were obtained from the modelling and simulations. The current utilization percentages for airspace load per hour and the static airspace load in the interested airspace were found to be 6.64% and 11.21% respectively. Our research also studied how ADS-B would affect the time taken for aircraft to travel. 6000 flights departing from and landing at the airport were studied. New flight plans were simulated with improved flight paths due to the implementation of ADS-B, and flight times of all studied flights could be improved.

Road Traffic Anomaly Detection via Collaborative Path Inference from GPS Snippets

PubMed Central

Wang, Hongtao; Wen, Hui; Yi, Feng; Zhu, Hongsong; Sun, Limin

2017-01-01

Road traffic anomaly denotes a road segment that is anomalous in terms of traffic flow of vehicles. Detecting road traffic anomalies from GPS (Global Position System) snippets data is becoming critical in urban computing since they often suggest underlying events. However, the noisy and sparse nature of GPS snippets data have ushered multiple problems, which have prompted the detection of road traffic anomalies to be very challenging. To address these issues, we propose a two-stage solution which consists of two components: a Collaborative Path Inference (CPI) model and a Road Anomaly Test (RAT) model. CPI model performs path inference incorporating both static and dynamic features into a Conditional Random Field (CRF). Dynamic context features are learned collaboratively from large GPS snippets via a tensor decomposition technique. Then RAT calculates the anomalous degree for each road segment from the inferred fine-grained trajectories in given time intervals. We evaluated our method using a large scale real world dataset, which includes one-month GPS location data from more than eight thousand taxicabs in Beijing. The evaluation results show the advantages of our method beyond other baseline techniques. PMID:28282948

An improved car-following model from the perspective of driver’s forecast behavior

NASA Astrophysics Data System (ADS)

Liu, Da-Wei; Shi, Zhong-Ke; Ai, Wen-Huan

In this paper, a new car-following model considering effect of the driver’s forecast behavior is proposed based on the full velocity difference model (FVDM). Using the new model, we investigate the starting process of the vehicle motion under a traffic signal and find that the delay time of vehicle motion is reduced. Then the stability condition of the new model is derived and the modified Korteweg-de Vries (mKdV) equation is constructed to describe the traffic behavior near the critical point. Numerical simulation is compatible with the analysis of theory such as density wave, hysteresis loop, which shows that the new model is reasonable. The results show that considering the effect of driver’s forecast behavior can help to enhance the stability of traffic flow.

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.

Two-vehicle injury severity models based on integration of pavement management and traffic engineering factors.

PubMed

Jiang, Ximiao; Huang, Baoshan; Yan, Xuedong; Zaretzki, Russell L; Richards, Stephen

2013-01-01

The severity of traffic-related injuries has been studied by many researchers in recent decades. However, the evaluation of many factors is still in dispute and, until this point, few studies have taken into account pavement management factors as points of interest. The objective of this article is to evaluate the combined influences of pavement management factors and traditional traffic engineering factors on the injury severity of 2-vehicle crashes. This study examines 2-vehicle rear-end, sideswipe, and angle collisions that occurred on Tennessee state routes from 2004 to 2008. Both the traditional ordered probit (OP) model and Bayesian ordered probit (BOP) model with weak informative prior were fitted for each collision type. The performances of these models were evaluated based on the parameter estimates and deviances. The results indicated that pavement management factors played identical roles in all 3 collision types. Pavement serviceability produces significant positive effects on the severity of injuries. The pavement distress index (PDI), rutting depth (RD), and rutting depth difference between right and left wheels (RD_df) were not significant in any of these 3 collision types. The effects of traffic engineering factors varied across collision types, except that a few were consistently significant in all 3 collision types, such as annual average daily traffic (AADT), rural-urban location, speed limit, peaking hour, and light condition. The findings of this study indicated that improved pavement quality does not necessarily lessen the severity of injuries when a 2-vehicle crash occurs. The effects of traffic engineering factors are not universal but vary by the type of crash. The study also found that the BOP model with a weak informative prior can be used as an alternative but was not superior to the traditional OP model in terms of overall performance.

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

Yang, Huan; Cheng, Liang; Chuah, Mooi Choo

In the generation, transmission, and distribution sectors of the smart grid, intelligence of field devices is realized by programmable logic controllers (PLCs). Many smart-grid subsystems are essentially cyber-physical energy systems (CPES): For instance, the power system process (i.e., the physical part) within a substation is monitored and controlled by a SCADA network with hosts running miscellaneous applications (i.e., the cyber part). To study the interactions between the cyber and physical components of a CPES, several co-simulation platforms have been proposed. However, the network simulators/emulators of these platforms do not include a detailed traffic model that takes into account the impactsmore » of the execution model of PLCs on traffic characteristics. As a result, network traces generated by co-simulation only reveal the impacts of the physical process on the contents of the traffic generated by SCADA hosts, whereas the distinction between PLCs and computing nodes (e.g., a hardened computer running a process visualization application) has been overlooked. To generate realistic network traces using co-simulation for the design and evaluation of applications relying on accurate traffic profiles, it is necessary to establish a traffic model for PLCs. In this work, we propose a parameterized model for PLCs that can be incorporated into existing co-simulation platforms. We focus on the DNP3 subsystem of slave PLCs, which automates the processing of packets from the DNP3 master. To validate our approach, we extract model parameters from both the configuration and network traces of real PLCs. Simulated network traces are generated and compared against those from PLCs. Our evaluation shows that our proposed model captures the essential traffic characteristics of DNP3 slave PLCs, which can be used to extend existing co-simulation platforms and gain further insights into the behaviors of CPES.« less

Traffic experiment reveals the nature of car-following.

PubMed

Jiang, Rui; Hu, Mao-Bin; Zhang, H M; Gao, Zi-You; Jia, Bin; Wu, Qing-Song; Wang, Bing; Yang, Ming

2014-01-01

As a typical self-driven many-particle system far from equilibrium, traffic flow exhibits diverse fascinating non-equilibrium phenomena, most of which are closely related to traffic flow stability and specifically the growth/dissipation pattern of disturbances. However, the traffic theories have been controversial due to a lack of precise traffic data. We have studied traffic flow from a new perspective by carrying out large-scale car-following experiment on an open road section, which overcomes the intrinsic deficiency of empirical observations. The experiment has shown clearly the nature of car-following, which runs against the traditional traffic flow theory. Simulations show that by removing the fundamental notion in the traditional car-following models and allowing the traffic state to span a two-dimensional region in velocity-spacing plane, the growth pattern of disturbances has changed qualitatively and becomes qualitatively or even quantitatively in consistent with that observed in the experiment.

Traffic Experiment Reveals the Nature of Car-Following

PubMed Central

Jiang, Rui; Hu, Mao-Bin; Zhang, H. M.; Gao, Zi-You; Jia, Bin; Wu, Qing-Song; Wang, Bing; Yang, Ming

2014-01-01

As a typical self-driven many-particle system far from equilibrium, traffic flow exhibits diverse fascinating non-equilibrium phenomena, most of which are closely related to traffic flow stability and specifically the growth/dissipation pattern of disturbances. However, the traffic theories have been controversial due to a lack of precise traffic data. We have studied traffic flow from a new perspective by carrying out large-scale car-following experiment on an open road section, which overcomes the intrinsic deficiency of empirical observations. The experiment has shown clearly the nature of car-following, which runs against the traditional traffic flow theory. Simulations show that by removing the fundamental notion in the traditional car-following models and allowing the traffic state to span a two-dimensional region in velocity-spacing plane, the growth pattern of disturbances has changed qualitatively and becomes qualitatively or even quantitatively in consistent with that observed in the experiment. PMID:24740284

Prediction based active ramp metering control strategy with mobility and safety assessment

NASA Astrophysics Data System (ADS)

Fang, Jie; Tu, Lili

2018-04-01

Ramp metering is one of the most direct and efficient motorway traffic flow management measures so as to improve traffic conditions. However, owing to short of traffic conditions prediction, in earlier studies, the impact on traffic flow dynamics of the applied RM control was not quantitatively evaluated. In this study, a RM control algorithm adopting Model Predictive Control (MPC) framework to predict and assess future traffic conditions, which taking both the current traffic conditions and the RM-controlled future traffic states into consideration, was presented. The designed RM control algorithm targets at optimizing the network mobility and safety performance. The designed algorithm is evaluated in a field-data-based simulation. Through comparing the presented algorithm controlled scenario with the uncontrolled scenario, it was proved that the proposed RM control algorithm can effectively relieve the congestion of traffic network with no significant compromises in safety aspect.

Structural equation modeling of the inflammatory response to traffic air pollution

PubMed Central

Baja, Emmanuel S.; Schwartz, Joel D.; Coull, Brent A.; Wellenius, Gregory A.; Vokonas, Pantel S.; Suh, Helen H.

2015-01-01

Several epidemiological studies have reported conflicting results on the effect of traffic-related pollutants on markers of inflammation. In a Bayesian framework, we examined the effect of traffic pollution on inflammation using structural equation models (SEMs). We studied measurements of C-reactive protein (CRP), soluble vascular cell adhesion molecule-1 (sVCAM-1), and soluble intracellular adhesion molecule-1 (sICAM-1) for 749 elderly men from the Normative Aging Study. Using repeated measures SEMs, we fit a latent variable for traffic pollution that is reflected by levels of black carbon, carbon monoxide, nitrogen monoxide and nitrogen dioxide to estimate its effect on a latent variable for inflammation that included sICAM-1, sVCAM-1 and CRP. Exposure periods were assessed using 1-, 2-, 3-, 7-, 14- and 30-day moving averages previsit. We compared our findings using SEMs with those obtained using linear mixed models. Traffic pollution was related to increased inflammation for 3-, 7-, 14- and 30-day exposure periods. An inter-quartile range increase in traffic pollution was associated with a 2.3% (95% posterior interval (PI): 0.0–4.7%) increase in inflammation for the 3-day moving average, with the most significant association observed for the 30-day moving average (23.9%; 95% PI: 13.9–36.7%). Traffic pollution adversely impacts inflammation in the elderly. SEMs in a Bayesian framework can comprehensively incorporate multiple pollutants and health outcomes simultaneously in air pollution–cardiovascular epidemiological studies. PMID:23232970

High blood pressure and long-term exposure to indoor noise and air pollution from road traffic.

PubMed

Foraster, Maria; Künzli, Nino; Aguilera, Inmaculada; Rivera, Marcela; Agis, David; Vila, Joan; Bouso, Laura; Deltell, Alexandre; Marrugat, Jaume; Ramos, Rafel; Sunyer, Jordi; Elosua, Roberto; Basagaña, Xavier

2014-11-01

Traffic noise has been associated with prevalence of hypertension, but reports are inconsistent for blood pressure (BP). To ascertain noise effects and to disentangle them from those suspected to be from traffic-related air pollution, it may be essential to estimate people's noise exposure indoors in bedrooms. We analyzed associations between long-term exposure to indoor traffic noise in bedrooms and prevalent hypertension and systolic (SBP) and diastolic (DBP) BP, considering long-term exposure to outdoor nitrogen dioxide (NO2). We evaluated 1,926 cohort participants at baseline (years 2003-2006; Girona, Spain). Outdoor annual average levels of nighttime traffic noise (Lnight) and NO2 were estimated at postal addresses with a detailed traffic noise model and a land-use regression model, respectively. Individual indoor traffic Lnight levels were derived from outdoor Lnight with application of insulations provided by reported noise-reducing factors. We assessed associations for hypertension and BP with multi-exposure logistic and linear regression models, respectively. Median levels were 27.1 dB(A) (indoor Lnight), 56.7 dB(A) (outdoor Lnight), and 26.8 μg/m3 (NO2). Spearman correlations between outdoor and indoor Lnight with NO2 were 0.75 and 0.23, respectively. Indoor Lnight was associated both with hypertension (OR = 1.06; 95% CI: 0.99, 1.13) and SBP (β = 0.72; 95% CI: 0.29, 1.15) per 5 dB(A); and NO2 was associated with hypertension (OR = 1.16; 95% CI: 0.99, 1.36), SBP (β = 1.23; 95% CI: 0.21, 2.25), and DBP (β⊇= 0.56; 95% CI: -0.03, 1.14) per 10 μg/m3. In the outdoor noise model, Lnight was associated only with hypertension and NO2 with BP only. The indoor noise-SBP association was stronger and statistically significant with a threshold at 30 dB(A). Long-term exposure to indoor traffic noise was associated with prevalent hypertension and SBP, independently of NO2. Associations were less consistent for outdoor traffic Lnight and likely affected by collinearity.

A Compendium Of Traffic Model Validation Documentation And Recommendations - Phase 1 - Tasks A-H

DOT National Transportation Integrated Search

1996-12-01

THE INTENT OF THIS REPORT IS TO CONSOLIDATE THE DOCUMENTATION DELIVERED TO FHWA FOR THE DATABASES FOR ASSESSMENT OF OPERATION TESTS AND TRAFFIC MODELS CONTRACT. : SOME INTRODUCTORY REMARKS ARE REQUIRED TO UNDERSTAND THE RATIONAL USED IN THE WHITE ...

Methodology and guidelines for regulating traffic flows under air quality constraints in metropolitan areas.

DOT National Transportation Integrated Search

2010-02-01

This project developed a methodology to couple a new pollutant dispersion model with a traffic : assignment process to contain air pollution while maximizing mobility. The overall objective of the air : quality modeling part of the project is to deve...

Intelligent traffic signals : extending the range of self-organization in the BML model.

DOT National Transportation Integrated Search

2013-04-01

The two-dimensional traffic model of Biham, Middleton and Levine (Phys. Rev. A, 1992) is : a simple cellular automaton that exhibits a wide range of complex behavior. It consists of both : northbound and eastbound cars traveling on a rectangular arra...

Air Quality Modeling of Traffic-related Air Pollutants for the NEXUS Study

EPA Science Inventory

The paper presents the results of the model applications to estimate exposure metrics in support of an epidemiologic study in Detroit, Michigan. A major challenge in traffic-related air pollution exposure studies is the lack of information regarding pollutant exposure characteriz...

Simulation and analysis of traffic flow based on cellular automaton

NASA Astrophysics Data System (ADS)

Ren, Xianping; Liu, Xia

2018-03-01

In this paper, single-lane and two-lane traffic model are established based on cellular automaton. Different values of vehicle arrival rate at the entrance and vehicle departure rate at the exit are set to analyze their effects on density, average speed and traffic flow. If the road exit is unblocked, vehicles can pass through the road smoothly despite of the arrival rate at the entrance. If vehicles enter into the road continuously, the traffic condition is varied with the departure rate at the exit. To avoid traffic jam, reasonable vehicle departure rate should be adopted.

Self-organized criticality in asymmetric exclusion model with noise for freeway traffic

NASA Astrophysics Data System (ADS)

Nagatani, Takashi

1995-02-01

The one-dimensional asymmetric simple-exclusion model with open boundaries for parallel update is extended to take into account temporary stopping of particles. The model presents the traffic flow on a highway with temporary deceleration of cars. Introducing temporary stopping into the asymmetric simple-exclusion model drives the system asymptotically into a steady state exhibiting a self-organized criticality. In the self-organized critical state, start-stop waves (or traffic jams) appear with various sizes (or lifetimes). The typical interval < s>between consecutive jams scales as < s> ≃ Lv with v = 0.51 ± 0.05 where L is the system size. It is shown that the cumulative jam-interval distribution Ns( L) satisfies the finite-size scaling form ( Ns( L) ≃ L- vf( s/ Lv). Also, the typical lifetime ≃ Lv‧ with v‧ = 0.52 ± 0.05. The cumulative distribution Nm( L) of lifetimes satisfies the finite-size scaling form Nm( L)≃ L-1g( m/ Lv‧).

Prediction of Particle Concentration using Traffic Emission Model

NASA Astrophysics Data System (ADS)

He, Hong-di; Lu, Jane Wei-zhen

2010-05-01

Vehicle emission is regarded as one of major sources of air pollution in urban area. Much attention has been addressed on it especially at traffic intersection. At intersection, vehicles frequently stop with idling engine during the red time and speed-up rapidly in the green time, which result in a high velocity fluctuation and produce extra pollutants to the surrounding air. To deeply understand such process, a semi-empirical model for predicting the changing effect of traffic flow patterns on particulate concentrations is proposed. The performance of the model is evaluated using the correlation coefficient and other parameters. From the results, the correlation coefficients in morning and afternoon data were found to be 0.86 an 0.73 respectively, which implies that the semi-empirical model for morning and afternoon data are 86% and 73% error free. Due to less affected by possible factors such as traffic volume and movement of pedestrian, the dispersion of the particulate matter in the morning is smaller and then contributes to higher performance than that in the afternoon.

Transforming GIS data into functional road models for large-scale traffic simulation.

PubMed

Wilkie, David; Sewall, Jason; Lin, Ming C

2012-06-01

There exists a vast amount of geographic information system (GIS) data that model road networks around the world as polylines with attributes. In this form, the data are insufficient for applications such as simulation and 3D visualization-tools which will grow in power and demand as sensor data become more pervasive and as governments try to optimize their existing physical infrastructure. In this paper, we propose an efficient method for enhancing a road map from a GIS database to create a geometrically and topologically consistent 3D model to be used in real-time traffic simulation, interactive visualization of virtual worlds, and autonomous vehicle navigation. The resulting representation provides important road features for traffic simulations, including ramps, highways, overpasses, legal merge zones, and intersections with arbitrary states, and it is independent of the simulation methodologies. We test the 3D models of road networks generated by our algorithm on real-time traffic simulation using both macroscopic and microscopic techniques.

The relationship between social capital and traffic law violations: Israeli Arabs as a case study.

PubMed

Obeid, Samira; Gitelman, Victoria; Baron-Epel, Orna

2014-10-01

Social aspects of a community may be correlated with driver's involvement in road traffic accidents. This study focused on examining this association in the context of the social capital theory. A survey of 600 Arab drivers living in 19 towns and villages was conducted using a face-to-face interview. Structural equation modeling was applied to explore paths of associations between the model components. Most of the proposed relationships in the path model were found to be significant, where the model explained 37% of the variation. The results indicate that only volunteering and reciprocity have direct correlations with traffic law violations. While the other correlations (except political involvement), were mediated by attitudes toward traffic laws violation. Hence, it can be concluded that it is not always possible to generalize the positive mechanisms of the social capital theory, and in certain populations such as the Arab minority it can give undesirable results. Copyright © 2014 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.

Asthma morbidity and ambient air pollution: effect modification by residential traffic-related air pollution.

PubMed

Delfino, Ralph J; Wu, Jun; Tjoa, Thomas; Gullesserian, Sevan K; Nickerson, Bruce; Gillen, Daniel L

2014-01-01

Ambient air pollution has been associated with asthma-related hospital admissions and emergency department visits (hospital encounters). We hypothesized that higher individual exposure to residential traffic-related air pollutants would enhance these associations. We studied 11,390 asthma-related hospital encounters among 7492 subjects 0-18 years of age living in Orange County, California. Ambient exposures were measured at regional air monitoring stations. Seasonal average traffic-related exposures (PM2.5, ultrafine particles, NOx, and CO) were estimated near subjects' geocoded residences for 6-month warm and cool seasonal periods, using dispersion models based on local traffic within 500 m radii. Associations were tested in case-crossover conditional logistic regression models adjusted for temperature and humidity. We assessed effect modification by seasonal residential traffic-related air pollution exposures above and below median dispersion-modeled exposures. Secondary analyses considered effect modification by traffic exposures within race/ethnicity and insurance group strata. Asthma morbidity was positively associated with daily ambient O3 and PM2.5 in warm seasons and with CO, NOx, and PM2.5 in cool seasons. Associations with CO, NOx, and PM2.5 were stronger among subjects living at residences with above-median traffic-related exposures, especially in cool seasons. Secondary analyses showed no consistent differences in association, and 95% confidence intervals were wide, indicating a lack of precision for estimating these highly stratified associations. Associations of asthma with ambient air pollution were enhanced among subjects living in homes with high traffic-related air pollution. This may be because of increased susceptibility (greater asthma severity) or increased vulnerability (meteorologic amplification of local vs. correlated ambient exposures).

How to determine an optimal threshold to classify real-time crash-prone traffic conditions?

PubMed

Yang, Kui; Yu, Rongjie; Wang, Xuesong; Quddus, Mohammed; Xue, Lifang

2018-08-01

One of the proactive approaches in reducing traffic crashes is to identify hazardous traffic conditions that may lead to a traffic crash, known as real-time crash prediction. Threshold selection is one of the essential steps of real-time crash prediction. And it provides the cut-off point for the posterior probability which is used to separate potential crash warnings against normal traffic conditions, after the outcome of the probability of a crash occurring given a specific traffic condition on the basis of crash risk evaluation models. There is however a dearth of research that focuses on how to effectively determine an optimal threshold. And only when discussing the predictive performance of the models, a few studies utilized subjective methods to choose the threshold. The subjective methods cannot automatically identify the optimal thresholds in different traffic and weather conditions in real application. Thus, a theoretical method to select the threshold value is necessary for the sake of avoiding subjective judgments. The purpose of this study is to provide a theoretical method for automatically identifying the optimal threshold. Considering the random effects of variable factors across all roadway segments, the mixed logit model was utilized to develop the crash risk evaluation model and further evaluate the crash risk. Cross-entropy, between-class variance and other theories were employed and investigated to empirically identify the optimal threshold. And K-fold cross-validation was used to validate the performance of proposed threshold selection methods with the help of several evaluation criteria. The results indicate that (i) the mixed logit model can obtain a good performance; (ii) the classification performance of the threshold selected by the minimum cross-entropy method outperforms the other methods according to the criteria. This method can be well-behaved to automatically identify thresholds in crash prediction, by minimizing the cross entropy between the original dataset with continuous probability of a crash occurring and the binarized dataset after using the thresholds to separate potential crash warnings against normal traffic conditions. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

Dispersion Modeling of Traffic-Related Air Pollutant Exposures and Health Effects among Children with Asthma in Detroit, Michigan

EPA Science Inventory

Vehicular traffic is a major source of ambient air pollution in urban areas, and traffic-related air pollutants, including carbon monoxide, nitrogen oxides, particulate matter under 2.5 microns in diameter (PM_2.5) and diesel exhaust emissions, have been associated with...

Development of a model performance-based sign sheeting specification based on the evaluation of nighttime traffic signs using legibility and eye-tracker data.

DOT National Transportation Integrated Search

2010-09-01

This project focused on the evaluation of traffic sign sheeting performance in terms of meeting the nighttime : driver needs. The goal was to develop a nighttime driver needs specification for traffic signs. The : researchers used nighttime sign legi...

Coarse analysis of collective behaviors: Bifurcation analysis of the optimal velocity model for traffic jam formation

NASA Astrophysics Data System (ADS)

Miura, Yasunari; Sugiyama, Yuki

2017-12-01

We present a general method for analyzing macroscopic collective phenomena observed in many-body systems. For this purpose, we employ diffusion maps, which are one of the dimensionality-reduction techniques, and systematically define a few relevant coarse-grained variables for describing macroscopic phenomena. The time evolution of macroscopic behavior is described as a trajectory in the low-dimensional space constructed by these coarse variables. We apply this method to the analysis of the traffic model, called the optimal velocity model, and reveal a bifurcation structure, which features a transition to the emergence of a moving cluster as a traffic jam.

An extended car-following model to describe connected traffic dynamics under cyberattacks

NASA Astrophysics Data System (ADS)

Wang, Pengcheng; Yu, Guizhen; Wu, Xinkai; Qin, Hongmao; Wang, Yunpeng

2018-04-01

In this paper, the impacts of the potential cyberattacks on vehicles are modeled through an extended car-following model. To better understand the mechanism of traffic disturbance under cyberattacks, the linear and nonlinear stability analysis are conducted respectively. Particularly, linear stability analysis is performed to obtain different neutral stability conditions with various parameters; and nonlinear stability analysis is carried out by using reductive perturbation method to derive the soliton solution of the modified Korteweg de Vries equation (mKdV) near the critical point, which is used to draw coexisting stability lines. Furthermore, by applying linear and nonlinear stability analysis, traffic flow state can be divided into three states, i.e., stable, metastable and unstable states which are useful to describe shockwave dynamics and driving behaviors under cyberattacks. The theoretical results show that the proposed car-following model is capable of successfully describing the car-following behavior of connected vehicles with cyberattacks. Finally, numerical simulation using real values has confirmed the validity of theoretical analysis. The results further demonstrate our model can be used to help avoid collisions and relieve traffic congestion with cybersecurity threats.

Traffic Flow Management Using Aggregate Flow Models and the Development of Disaggregation Methods

NASA Technical Reports Server (NTRS)

Sun, Dengfeng; Sridhar, Banavar; Grabbe, Shon

2010-01-01

A linear time-varying aggregate traffic flow model can be used to develop Traffic Flow Management (tfm) strategies based on optimization algorithms. However, there are no methods available in the literature to translate these aggregate solutions into actions involving individual aircraft. This paper describes and implements a computationally efficient disaggregation algorithm, which converts an aggregate (flow-based) solution to a flight-specific control action. Numerical results generated by the optimization method and the disaggregation algorithm are presented and illustrated by applying them to generate TFM schedules for a typical day in the U.S. National Airspace System. The results show that the disaggregation algorithm generates control actions for individual flights while keeping the air traffic behavior very close to the optimal solution.

Air Quality Modeling in Support of the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS)

EPA Science Inventory

A major challenge in traffic-related air pollution exposure studies is the lack of information regarding pollutant exposure characterization. Air quality modeling can provide spatially and temporally varying exposure estimates for examining relationships between traffic-related a...

Modeling and Impacts of Traffic Emissions on Air Toxics Concentrations near Roadways

EPA Science Inventory

The dispersion formulation incorporated in the U.S. Environmental Protection Agency’s AERMOD regulatory dispersion model is used to estimate the contribution of traffic-generated emissions of select VOCs – benzene, 1,3-butadiene, toluene – to ambient air concentrations at downwin...

Traffic speed data imputation method based on tensor completion.

PubMed

Ran, Bin; Tan, Huachun; Feng, Jianshuai; Liu, Ying; Wang, Wuhong

2015-01-01

Traffic speed data plays a key role in Intelligent Transportation Systems (ITS); however, missing traffic data would affect the performance of ITS as well as Advanced Traveler Information Systems (ATIS). In this paper, we handle this issue by a novel tensor-based imputation approach. Specifically, tensor pattern is adopted for modeling traffic speed data and then High accurate Low Rank Tensor Completion (HaLRTC), an efficient tensor completion method, is employed to estimate the missing traffic speed data. This proposed method is able to recover missing entries from given entries, which may be noisy, considering severe fluctuation of traffic speed data compared with traffic volume. The proposed method is evaluated on Performance Measurement System (PeMS) database, and the experimental results show the superiority of the proposed approach over state-of-the-art baseline approaches.

Traffic Speed Data Imputation Method Based on Tensor Completion

PubMed Central

Ran, Bin; Feng, Jianshuai; Liu, Ying; Wang, Wuhong

2015-01-01

Traffic speed data plays a key role in Intelligent Transportation Systems (ITS); however, missing traffic data would affect the performance of ITS as well as Advanced Traveler Information Systems (ATIS). In this paper, we handle this issue by a novel tensor-based imputation approach. Specifically, tensor pattern is adopted for modeling traffic speed data and then High accurate Low Rank Tensor Completion (HaLRTC), an efficient tensor completion method, is employed to estimate the missing traffic speed data. This proposed method is able to recover missing entries from given entries, which may be noisy, considering severe fluctuation of traffic speed data compared with traffic volume. The proposed method is evaluated on Performance Measurement System (PeMS) database, and the experimental results show the superiority of the proposed approach over state-of-the-art baseline approaches. PMID:25866501

Residential exposure to traffic noise and leisure-time sports - A population-based study.

PubMed

Roswall, Nina; Ammitzbøll, Gunn; Christensen, Jeppe Schultz; Raaschou-Nielsen, Ole; Jensen, Steen Solvang; Tjønneland, Anne; Sørensen, Mette

2017-08-01

Traffic levels have been found a significant environmental predictor for physical inactivity. A recent study suggested that traffic noise annoyance was associated with lower physical activity. We investigated associations between modelled residential traffic noise and leisure-time sports. In the Diet, Cancer and Health cohort, we performed cross-sectional analyses using data from the baseline questionnaire (1993-97), and longitudinal analyses of change between baseline and follow-up (2000-02). People reported participation (yes/no) and hours of leisure-time sport, from which we calculated MET hrs/week. Present and historical addresses from 1987 to 2002 were found in national registries, and traffic noise was modelled 1 and 5 years before enrolment, and from baseline to follow-up. Analyses were performed using logistic and linear regression. Traffic noise exposure 5 years before baseline was associated with higher prevalence odds ratio of non-participation in leisure-time sports; significantly for road traffic noise (odds ratio (OR): 1.10; 95% confidence interval (CI): 1.07-1.13) and borderline for railway noise (OR: 1.03; 95% CI: 0.99-1.07), per 10dB. In longitudinal analyses, a 10dB higher road traffic noise was associated with a higher prevalence odds ratio of ceasing (OR: 1.12; 95% CI: 1.07-1.18) and a lower prevalence odds ratio of initiating (OR: 0.92; 95% CI: 0.87-0.96) leisure-time sports. Exposure to railway noise was negatively associated with baseline MET hrs/week, whereas no association was found in longitudinal analyses, or for road traffic noise. The study suggests that long-term exposure to residential road traffic noise is negatively associated with leisure-time sports. Results for railway noise were less consistent. Copyright © 2017 Elsevier GmbH. All rights reserved.

Modeling and Control of Airport Queueing Dynamics under Severe Flow Restrictions

NASA Technical Reports Server (NTRS)

Carr, Francis; Evans, Antony; Clarke, John-Paul; Deron, Eric

2003-01-01

Based on field observations and interviews with controllers at BOS and EWR, we identify the closure of local departure fixes as the most severe class of airport departure restrictions. A set of simple queueing dynamics and traffic rules are developed to model departure traffic under such restrictions. The validity of the proposed model is tested via Monte Carlo simulation against 10 hours of actual operations data collected during a case-study at EWR on June 29,2000. In general, the model successfully reproduces the aggregate departure congestion. An analysis of the average error over 40 simulation runs indicates that flow-rate restrictions also significantly impact departure traffic; work is underway to capture these effects. Several applications and what-if scenarios are discussed for future evaluation using the calibrated model.

Nature of the Congested Traffic and Quasi-steady States of the General Motor Models

NASA Astrophysics Data System (ADS)

Yang, Bo; Xu, Xihua; Pang, John Z. F.; Monterola, Christopher

2015-03-01

We look at the general motor (GM) class microscopic traffic models and analyze some of the universal features of the (multi-)cluster solutions, including the emergence of an intrinsic scale and the quasisoliton dynamics. We show that the GM models can capture the essential physics of the real traffic dynamics, especially the phase transition from the free flow to the congested phase, from which the wide moving jams emerges (the F-S-J transition pioneered by B.S. Kerner). In particular, the congested phase can be associated with either the multi-cluster quasi-steady states, or their more homogeneous precursor states. In both cases the states can last for a long time, and the narrow clusters will eventually grow and merge, leading to the formation of the wide moving jams. We present a general method to fit the empirical parameters so that both quantitative and qualitative macroscopic empirical features can be reproduced with a minimal GM model. We present numerical results for the traffic dynamics both with and without the bottleneck, including various types of spontaneous and induced ``synchronized flow,'' as well as the evolution of wide moving jams. We also discuss its implications to the nature of different phases in traffic dynamics.

Physics of traffic gridlock in a city.

PubMed

Kerner, Boris S

2011-10-01

Based on simulations of stochastic three-phase and two-phase traffic flow models, we reveal that at a signalized city intersection under small link inflow rates at which a vehicle queue developed during the red phase of the light signal dissolves fully during the green phase, i.e., no traffic gridlock should be expected, nevertheless, spontaneous traffic breakdown with subsequent city gridlock occurs with some probability after a random time delay. In most cases, this traffic breakdown is initiated by a phase transition from free flow to a synchronized flow occurring upstream of the queue at the light signal. The probability of traffic breakdown at the light signal is an increasing function of the link inflow rate and duration of the red phase of the light signal.

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.

Vehicular traffic noise prediction using soft computing approach.

PubMed

Singh, Daljeet; Nigam, S P; Agrawal, V P; Kumar, Maneek

2016-12-01

A new approach for the development of vehicular traffic noise prediction models is presented. Four different soft computing methods, namely, Generalized Linear Model, Decision Trees, Random Forests and Neural Networks, have been used to develop models to predict the hourly equivalent continuous sound pressure level, Leq, at different locations in the Patiala city in India. The input variables include the traffic volume per hour, percentage of heavy vehicles and average speed of vehicles. The performance of the four models is compared on the basis of performance criteria of coefficient of determination, mean square error and accuracy. 10-fold cross validation is done to check the stability of the Random Forest model, which gave the best results. A t-test is performed to check the fit of the model with the field data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Modeling and performance analysis of QoS data

NASA Astrophysics Data System (ADS)

Strzeciwilk, Dariusz; Zuberek, Włodzimierz M.

2016-09-01

The article presents the results of modeling and analysis of data transmission performance on systems that support quality of service. Models are designed and tested, taking into account multiservice network architecture, i.e. supporting the transmission of data related to different classes of traffic. Studied were mechanisms of traffic shaping systems, which are based on the Priority Queuing with an integrated source of data and the various sources of data that is generated. Discussed were the basic problems of the architecture supporting QoS and queuing systems. Designed and built were models based on Petri nets, supported by temporal logics. The use of simulation tools was to verify the mechanisms of shaping traffic with the applied queuing algorithms. It is shown that temporal models of Petri nets can be effectively used in the modeling and analysis of the performance of computer networks.

Path-preference cellular-automaton model for traffic flow through transit points and its application to the transcription process in human cells.

PubMed

Ohta, Yoshihiro; Nishiyama, Akinobu; Wada, Yoichiro; Ruan, Yijun; Kodama, Tatsuhiko; Tsuboi, Takashi; Tokihiro, Tetsuji; Ihara, Sigeo

2012-08-01

We all use path routing everyday as we take shortcuts to avoid traffic jams, or by using faster traffic means. Previous models of traffic flow of RNA polymerase II (RNAPII) during transcription, however, were restricted to one dimension along the DNA template. Here we report the modeling and application of traffic flow in transcription that allows preferential paths of different dimensions only restricted to visit some transit points, as previously introduced between the 5' and 3' end of the gene. According to its position, an RNAPII protein molecule prefers paths obeying two types of time-evolution rules. One is an asymmetric simple exclusion process (ASEP) along DNA, and the other is a three-dimensional jump between transit points in DNA where RNAPIIs are staying. Simulations based on our model, and comparison experimental results, reveal how RNAPII molecules are distributed at the DNA-loop-formation-related protein binding sites as well as CTCF insulator proteins (or exons). As time passes after the stimulation, the RNAPII density at these sites becomes higher. Apparent far-distance jumps in one dimension are realized by short-range three-dimensional jumps between DNA loops. We confirm the above conjecture by applying our model calculation to the SAMD4A gene by comparing the experimental results. Our probabilistic model provides possible scenarios for assembling RNAPII molecules into transcription factories, where RNAPII and related proteins cooperatively transcribe DNA.

Exposure to traffic-related air pollution during pregnancy and term low birth weight: estimation of causal associations in a semiparametric model.

PubMed

Padula, Amy M; Mortimer, Kathleen; Hubbard, Alan; Lurmann, Frederick; Jerrett, Michael; Tager, Ira B

2012-11-01

Traffic-related air pollution is recognized as an important contributor to health problems. Epidemiologic analyses suggest that prenatal exposure to traffic-related air pollutants may be associated with adverse birth outcomes; however, there is insufficient evidence to conclude that the relation is causal. The Study of Air Pollution, Genetics and Early Life Events comprises all births to women living in 4 counties in California's San Joaquin Valley during the years 2000-2006. The probability of low birth weight among full-term infants in the population was estimated using machine learning and targeted maximum likelihood estimation for each quartile of traffic exposure during pregnancy. If everyone lived near high-volume freeways (approximated as the fourth quartile of traffic density), the estimated probability of term low birth weight would be 2.27% (95% confidence interval: 2.16, 2.38) as compared with 2.02% (95% confidence interval: 1.90, 2.12) if everyone lived near smaller local roads (first quartile of traffic density). Assessment of potentially causal associations, in the absence of arbitrary model assumptions applied to the data, should result in relatively unbiased estimates. The current results support findings from previous studies that prenatal exposure to traffic-related air pollution may adversely affect birth weight among full-term infants.

Implementing and Simulating Dynamic Traffic Assignment with Intelligent Transportation Systems in Cube Avenue

NASA Technical Reports Server (NTRS)

Foytik, Peter; Robinson, Mike

2010-01-01

As urban populations and traffic congestion levels increase, effective use of information and communication tools and intelligent transportation systems as becoming increasingly important in order to maximize the efficiency of transportation networks. The appropriate placement and employment of these tools within a network is critical to their effectiveness. This presentation proposes and demonstrates the use of a commercial transportation simulation tool to simulate dynamic traffic assignment and rerouting to model route modifications as a result of traffic information.

Traffic off-balancing algorithm for energy efficient networks

NASA Astrophysics Data System (ADS)

Kim, Junhyuk; Lee, Chankyun; Rhee, June-Koo Kevin

2011-12-01

Physical layer of high-end network system uses multiple interface arrays. Under the load-balancing perspective, light load can be distributed to multiple interfaces. However, it can cause energy inefficiency in terms of the number of poor utilization interfaces. To tackle this energy inefficiency, traffic off-balancing algorithm for traffic adaptive interface sleep/awake is investigated. As a reference model, 40G/100G Ethernet is investigated. We report that suggested algorithm can achieve energy efficiency while satisfying traffic transmission requirement.

Linear stability and nonlinear analyses of traffic waves for the general nonlinear car-following model with multi-time delays

NASA Astrophysics Data System (ADS)

Sun, Dihua; Chen, Dong; Zhao, Min; Liu, Weining; Zheng, Linjiang

2018-07-01

In this paper, the general nonlinear car-following model with multi-time delays is investigated in order to describe the reactions of vehicle to driving behavior. Platoon stability and string stability criteria are obtained for the general nonlinear car-following model. Burgers equation and Korteweg de Vries (KdV) equation and their solitary wave solutions are derived adopting the reductive perturbation method. We investigate the properties of typical optimal velocity model using both analytic and numerical methods, which estimates the impact of delays about the evolution of traffic congestion. The numerical results show that time delays in sensing relative movement is more sensitive to the stability of traffic flow than time delays in sensing host motion.

A Multiple Agent Model of Human Performance in Automated Air Traffic Control and Flight Management Operations

NASA Technical Reports Server (NTRS)

Corker, Kevin; Pisanich, Gregory; Condon, Gregory W. (Technical Monitor)

1995-01-01

A predictive model of human operator performance (flight crew and air traffic control (ATC)) has been developed and applied in order to evaluate the impact of automation developments in flight management and air traffic control. The model is used to predict the performance of a two person flight crew and the ATC operators generating and responding to clearances aided by the Center TRACON Automation System (CTAS). The purpose of the modeling is to support evaluation and design of automated aids for flight management and airspace management and to predict required changes in procedure both air and ground in response to advancing automation in both domains. Additional information is contained in the original extended abstract.

A Near-Road Modeling System for Community-Scale Assessments of Traffic-Related AirPollution in the United States

EPA Science Inventory

The Community Line Source (C-LINE) modeling system estimates emissions and dispersion of toxic air pollutants for roadways within the continental United States. It accesses publicly available traffic and meteorological datasets, and is optimized for use on community-sized areas (...

Graph Coloring Used to Model Traffic Lights.

ERIC Educational Resources Information Center

Williams, John

1992-01-01

Two scheduling problems, one involving setting up an examination schedule and the other describing traffic light problems, are modeled as colorings of graphs consisting of a set of vertices and edges. The chromatic number, the least number of colors necessary for coloring a graph, is employed in the solutions. (MDH)

Behavior-dependent Routing: Responding to Anomalies with Automated Low-cost Measures

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

Oehmen, Christopher S.; Carroll, Thomas E.; Paulson, Patrick R.

2015-10-12

This is a conference paper submission describing research and software implementation of a cybersecurity concept that uses behavior models to trigger changes in routing of network traffic. As user behavior deviates more and more from baseline models, traffic is routed through more elevated layers of analysis and control.

Highway traffic noise measurements at acoustically hard ground sites compared to predictions from FHWA's Traffic Noise Model

DOT National Transportation Integrated Search

2001-10-29

In order to better assess the accuracy and make recommendations on the use of TNM for the FHWA, : the Volpe Center Acoustics Facility is performing an extensive validation study. The study involves highway : noise data collection and TNM modeling for...

AHP-based spatial analysis of water quality impact assessment due to change in vehicular traffic caused by highway broadening in Sikkim Himalaya

NASA Astrophysics Data System (ADS)

Banerjee, Polash; Ghose, Mrinal Kanti; Pradhan, Ratika

2018-05-01

Spatial analysis of water quality impact assessment of highway projects in mountainous areas remains largely unexplored. A methodology is presented here for Spatial Water Quality Impact Assessment (SWQIA) due to highway-broadening-induced vehicular traffic change in the East district of Sikkim. Pollution load of the highway runoff was estimated using an Average Annual Daily Traffic-Based Empirical model in combination with mass balance model to predict pollution in the rivers within the study area. Spatial interpolation and overlay analysis were used for impact mapping. Analytic Hierarchy Process-Based Water Quality Status Index was used to prepare a composite impact map. Model validation criteria, cross-validation criteria, and spatial explicit sensitivity analysis show that the SWQIA model is robust. The study shows that vehicular traffic is a significant contributor to water pollution in the study area. The model is catering specifically to impact analysis of the concerned project. It can be an aid for decision support system for the project stakeholders. The applicability of SWQIA model needs to be explored and validated in the context of a larger set of water quality parameters and project scenarios at a greater spatial scale.

Model-Based Design of Air Traffic Controller-Automation Interaction

NASA Technical Reports Server (NTRS)

Romahn, Stephan; Callantine, Todd J.; Palmer, Everett A.; Null, Cynthia H. (Technical Monitor)

1998-01-01

A model of controller and automation activities was used to design the controller-automation interactions necessary to implement a new terminal area air traffic management concept. The model was then used to design a controller interface that provides the requisite information and functionality. Using data from a preliminary study, the Crew Activity Tracking System (CATS) was used to help validate the model as a computational tool for describing controller performance.

Input-Output Modeling and Control of the Departure Process of Congested Airports

NASA Technical Reports Server (NTRS)

Pujet, Nicolas; Delcaire, Bertrand; Feron, Eric

2003-01-01

A simple queueing model of busy airport departure operations is proposed. This model is calibrated and validated using available runway configuration and traffic data. The model is then used to evaluate preliminary control schemes aimed at alleviating departure traffic congestion on the airport surface. The potential impact of these control strategies on direct operating costs, environmental costs and overall delay is quantified and discussed.

Modeling of road traffic noise and estimated human exposure in Fulton County, Georgia, USA.

PubMed

Seong, Jeong C; Park, Tae H; Ko, Joon H; Chang, Seo I; Kim, Minho; Holt, James B; Mehdi, Mohammed R

2011-11-01

Environmental noise is a major source of public complaints. Noise in the community causes physical and socio-economic effects and has been shown to be related to adverse health impacts. Noise, however, has not been actively researched in the United States compared with the European Union countries in recent years. In this research, we aimed at modeling road traffic noise and analyzing human exposure in Fulton County, Georgia, United States. We modeled road traffic noise levels using the United States Department of Transportation Federal Highway Administration Traffic Noise Model implemented in SoundPLAN®. After analyzing noise levels with raster, vector and façade maps, we estimated human exposure to high noise levels. Accurate digital elevation models and building heights were derived from Light Detection And Ranging survey datasets and building footprint boundaries. Traffic datasets were collected from the Georgia Department of Transportation and the Atlanta Regional Commission. Noise level simulation was performed with 62 computers in a distributed computing environment. Finally, the noise-exposed population was calculated using geographic information system techniques. Results show that 48% of the total county population [N=870,166 residents] is potentially exposed to 55 dB(A) or higher noise levels during daytime. About 9% of the population is potentially exposed to 67 dB(A) or higher noises. At nighttime, 32% of the population is expected to be exposed to noise levels higher than 50 dB(A). This research shows that large-scale traffic noise estimation is possible with the help of various organizations. We believe that this research is a significant stepping stone for analyzing community health associated with noise exposures in the United States. Copyright © 2011 Elsevier Ltd. All rights reserved.

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.

Understanding the T2 traffic in CMS during Run-1

NASA Astrophysics Data System (ADS)

T, Wildish

2015-12-01

In the run-up to Run-1 CMS was operating its facilities according to the MONARC model, where data-transfers were strictly hierarchical in nature. Direct transfers between Tier-2 nodes was excluded, being perceived as operationally intensive and risky in an era where the network was expected to be a major source of errors. By the end of Run-1 wide-area networks were more capable and stable than originally anticipated. The original data-placement model was relaxed, and traffic was allowed between Tier-2 nodes. Tier-2 to Tier-2 traffic in 2012 already exceeded the amount of Tier-2 to Tier-1 traffic, so it clearly has the potential to become important in the future. Moreover, while Tier-2 to Tier-1 traffic is mostly upload of Monte Carlo data, the Tier-2 to Tier-2 traffic represents data moved in direct response to requests from the physics analysis community. As such, problems or delays there are more likely to have a direct impact on the user community. Tier-2 to Tier-2 traffic may also traverse parts of the WAN that are at the 'edge' of our network, with limited network capacity or reliability compared to, say, the Tier-0 to Tier-1 traffic which goes the over LHCOPN network. CMS is looking to exploit technologies that allow us to interact with the network fabric so that it can manage our traffic better for us, this we hope to achieve before the end of Run-2. Tier-2 to Tier-2 traffic would be the most interesting use-case for such traffic management, precisely because it is close to the users' analysis and far from the 'core' network infrastructure. As such, a better understanding of our Tier-2 to Tier-2 traffic is important. Knowing the characteristics of our data-flows can help us place our data more intelligently. Knowing how widely the data moves can help us anticipate the requirements for network capacity, and inform the dynamic data placement algorithms we expect to have in place for Run-2. This paper presents an analysis of the CMS Tier-2 traffic during Run 1.

Exposure to traffic noise and air pollution and risk for febrile seizure: a cohort study.

PubMed

Hjortebjerg, Dorrit; Nybo Andersen, Anne-Marie; Ketzel, Matthias; Raaschou-Nielsen, Ole; Sørensen, Mette

2018-03-25

Objectives Exposure to traffic noise and air pollution is suspected to increase susceptibility to viral infections - the main triggering factor for febrile seizures. No studies have examined these two exposures in relation to febrile seizures. We aimed to investigate whether exposure to road traffic noise and air pollution are associated with risk of febrile seizures in childhood. Methods From our study base of 51 465 singletons from a national birth cohort, we identified 2175 cases with febrile seizures using a nationwide registry. Residential address history from conception to six years of age were found in national registers, and road traffic noise (L den ) and air pollution (NO 2 ) were modeled for all addresses. Analyses were done using Cox proportional hazard model with adjustment for potential confounders, including mutual exposure adjustment. Results An interquartile range (IQR) increase in childhood exposure to road traffic noise and air pollution was associated with an 11% [incidence rate ratio (IRR) 1.11, 95% confidence interval (CI) 1.04-1.19) and 5% (IRR 1.05, 95% CI 1.02-1.07) higher risk for febrile seizures, respectively, after adjustment for potential confounders. Weaker tendencies were seen for pregnancy exposure. In models with mutual exposure adjustment, the estimates were slightly lower, with IRR of 1.08 (95% CI 1.00-1.16) and 1.03 (95% CI 0.99-1.06) per IQR increase in childhood exposure to road traffic noise and air pollution, respectively. Conclusions This study suggests that residential exposure to road traffic noise and air pollution is associated with higher risk for febrile seizures.

Tour time in a two-route traffic system controlled by signals

NASA Astrophysics Data System (ADS)

Nagatani, Takashi; Naito, Yuichi

2011-11-01

We study the dynamic behavior of vehicular traffic in a two-route system with a series of signals (traffic lights) at low density where the number of signals on route A is different from that on route B. We investigate the dependence of the tour time on the route for some strategies of signal control. The nonlinear dynamic model of a two-route traffic system controlled by signals is presented by nonlinear maps. The vehicular traffic exhibits a very complex behavior, depending on the cycle time, the phase difference, and the irregularity. The dependence of the tour time on the route choice is clarified for the signal strategies.

The relative efficiency of Iranian's rural traffic police: a three-stage DEA model.

PubMed

Rahimi, Habibollah; Soori, Hamid; Nazari, Seyed Saeed Hashemi; Motevalian, Seyed Abbas; Azar, Adel; Momeni, Eskandar; Javartani, Mehdi

2017-10-13

Road traffic Injuries (RTIs) as a health problem imposes governments to implement different interventions. Target achievement in this issue required effective and efficient measures. Efficiency evaluation of traffic police as one of the responsible administrators is necessary for resource management. Therefore, this study conducted to measure Iran's rural traffic police efficiency. This was an ecological study. To obtain pure efficiency score, three-stage DEA model was conducted with seven inputs and three output variables. At the first stage, crude efficiency score was measured with BCC-O model. Next, to extract the effects of socioeconomic, demographic, traffic count and road infrastructure as the environmental variables and statistical noise, the Stochastic Frontier Analysis (SFA) model was applied and the output values were modified according to similar environment and statistical noise conditions. Then, the pure efficiency score was measured using modified outputs and BCC-O model. In total, the efficiency score of 198 police stations from 24 provinces of 31 provinces were measured. The annual means (standard deviation) of damage, injury and fatal accidents were 247.7 (258.4), 184.9 (176.9), and 28.7 (19.5), respectively. Input averages were 5.9 (3.0) patrol teams, 0.5% (0.2) manpower proportions, 7.5 (2.9) patrol cars, 0.5 (1.3) motorcycles, 77,279.1 (46,794.7) penalties, 90.9 (2.8) cultural and educational activity score, 0.7 (2.4) speed cameras. The SFA model showed non-significant differences between police station performances and the most differences attributed to the environmental and random error. One-way main road, by road, traffic count and the number of household owning motorcycle had significant positive relations with inefficiency score. The length of freeway/highway and literacy rate variables had negative relations, significantly. Pure efficiency score was with mean of 0.95 and SD of 0.09. Iran's traffic police has potential opportunity to reduce RTIs. Adjusting police performance with environmental conditions is necessary. Capability of DEA method in setting quantitative targets for every station induces motivation for managers to reduce RTIs. Repetition of this study is recommended, annually.

Order and disorder in traffic and self-driven many-particle systems

NASA Astrophysics Data System (ADS)

Helbing, Dirk

2002-07-01

During the last decade, physicists have identified various spatio-temporal patterns of motion in vehicle and pedestrian traffic. Moreover, by applying and extending methods from statistical physics and non-linear dynamics, these have been successfully explained by means of self-driven many-particle models. Some of the questions now understood are the following: Why are vehicles sometimes stopped by so-called "phantom traffic jams," although they 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 of the traffic volume cause a lasting traffic jam? What is the origin of fluctuations in traffic systems and which consequences do they have? Why do pedestrians moving in opposite directions normally organize in lanes, while nervous crowds are "freezing by heating?" Why do panicking pedestrians produce dangerous deadlocks?

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.

SAE for the prediction of road traffic status from taxicab operating data and bus smart card data

NASA Astrophysics Data System (ADS)

Zhengfeng, Huang; Pengjun, Zheng; Wenjun, Xu; Gang, Ren

Road traffic status is significant for trip decision and traffic management, and thus should be predicted accurately. A contribution is that we consider multi-modal data for traffic status prediction than only using single source data. With the substantial data from Ningbo Passenger Transport Management Sector (NPTMS), we wished to determine whether it was possible to develop Stacked Autoencoders (SAEs) for accurately predicting road traffic status from taxicab operating data and bus smart card data. We show that SAE performed better than linear regression model and Back Propagation (BP) neural network for determining the relationship between road traffic status and those factors. In a 26-month data experiment using SAE, we show that it is possible to develop highly accurate predictions (91% test accuracy) of road traffic status from daily taxicab operating data and bus smart card data.

Social dilemma structure hidden behind traffic flow with route selection

NASA Astrophysics Data System (ADS)

Tanimoto, Jun; Nakamura, Kousuke

2016-10-01

Several traffic flows contain social dilemma structures. Herein, we explored a route-selection problem using a cellular automaton simulation dovetailed with evolutionary game theory. In our model, two classes of driver-agents coexist: D agents (defective strategy), which refer to traffic information for route selection to move fast, and C agents (cooperative strategy), which are insensitive to information and less inclined to move fast. Although no evidence suggests that the social dilemma structure in low density causes vehicles to move freely and that in high density causes traffic jams, we found a structure that corresponds to an n-person (multiplayer) Chicken (n-Chicken) game if the provided traffic information is inappropriate. If appropriate traffic information is given to the agents, the n-Chicken game can be solved. The information delivered to vehicles is crucial for easing the social dilemma due to urban traffic congestion when developing technologies to support the intelligent transportation system (ITS).

The impact of self-driving cars on existing transportation networks

NASA Astrophysics Data System (ADS)

Ji, Xiang

2018-04-01

In this paper, considering the usage of self-driving, I research the congestion problems of traffic networks from both macro and micro levels. Firstly, the macroscopic mathematical model is established using the Greenshields function, analytic hierarchy process and Monte Carlo simulation, where the congestion level is divided into five levels according to the average vehicle speed. The roads with an obvious congestion situation is investigated mainly and the traffic flow and topology of the roads are analyzed firstly. By processing the data, I propose a traffic congestion model. In the model, I assume that half of the non-self-driving cars only take the shortest route and the other half can choose the path randomly. While self-driving cars can obtain vehicle density data of each road and choose the path more reasonable. When the path traffic density exceeds specific value, it cannot be selected. To overcome the dimensional differences of data, I rate the paths by BORDA sorting. The Monte Carlo simulation of Cellular Automaton is used to obtain the negative feedback information of the density of the traffic network, where the vehicles are added into the road network one by one. I then analyze the influence of negative feedback information on path selection of intelligent cars. The conclusion is that the increase of the proportion of intelligent vehicles will make the road load more balanced, and the self-driving cars can avoid the peak and reduce the degree of road congestion. Combined with other models, the optimal self-driving ratio is about sixty-two percent. From the microscopic aspect, by using the single-lane traffic NS rule, another model is established to analyze the road Partition scheme. The self-driving traffic is more intelligent, and their cooperation can reduce the random deceleration probability. By the model, I get the different self-driving ratio of space-time distribution. I also simulate the case of making a lane separately for self-driving, compared to the former model. It is concluded that a single lane is more efficient in a certain interval. However, it is not recommended to offer a lane separately. However, the self-driving also faces the problem of hacker attacks and greater damage after fault. So, when self-driving ratio is higher than a certain value, the increase of traffic flow rate is small. In this article, that value is discussed, and the optimal proportion is determined. Finally, I give a nontechnical explanation of the problem.

Analysis of vehicle's safety envelope under car-following model

NASA Astrophysics Data System (ADS)

Tang, Tie-Qiao; Zhang, Jian; Chen, Liang; Shang, Hua-Yan

2017-05-01

In this paper, we propose an improved car-following model to explore the impacts of vehicle's two safety distances (i.e., the front safety distance and back safety distance) on the traffic safety during the starting process. The numerical results show that our model is prominently safer than the FVD (full velocity difference) model, i.e., our model is better than the FVD model from the perspective of the traffic safety, which shows that each driver should consider his two safety distances during his driving process.

Vehicle Modeling for Future Generation Transportation Simulation

DOT National Transportation Integrated Search

2009-05-10

Recent development of inter-vehicular wireless communication technologies have motivated many innovative applications aiming at significantly increasing traffic throughput and improving highway safety. Powerful traffic simulation is an indispensable ...

Modeling left-turn crash occurrence at signalized intersections by conflicting patterns.

PubMed

Wang, Xuesong; Abdel-Aty, Mohamed

2008-01-01

In order to better understand the underlying crash mechanisms, left-turn crashes occurring at 197 four-legged signalized intersections over 6 years were classified into nine patterns based on vehicle maneuvers and then were assigned to intersection approaches. Crash frequency of each pattern was modeled at the approach level by mainly using Generalized Estimating Equations (GEE) with the Negative Binomial as the link function to account for the correlation among the crash data. GEE with a binomial logit link function was also applied for patterns with fewer crashes. The Cumulative Residuals test shows that, for correlated left-turn crashes, GEE models usually outperformed basic Negative Binomial models. The estimation results show that there are obvious differences in the factors that cause the occurrence of different left-turn collision patterns. For example, for each pattern, the traffic flows to which the colliding vehicles belong are identified to be significant. The width of the crossing distance (represented by the number of through lanes on the opposing approach of the left-turning traffic) is associated with more left-turn traffic colliding with opposing through traffic (Pattern 5), but with less left-turning traffic colliding with near-side crossing through traffic (Pattern 8). The safety effectiveness of the left-turning signal is not consistent for different crash patterns; "protected" phasing is correlated with fewer Pattern 5 crashes, but with more Pattern 8 crashes. The study indicates that in order to develop efficient countermeasures for left-turn crashes and improve safety at signalized intersections, left-turn crashes should be considered in different patterns.

Spatial distribution of traffic induced noise exposures in a US city: an analytic tool for assessing the health impacts of urban planning decisions

PubMed Central

Seto, Edmund Yet Wah; Holt, Ashley; Rivard, Tom; Bhatia, Rajiv

2007-01-01

Background: Vehicle traffic is the major source of noise in urban environments, which in turn has multiple impacts on health. In this paper we investigate the spatial distribution of community noise exposures and annoyance. Traffic data from the City of San Francisco were used to model noise exposure by neighborhood and road type. Remote sensing data were used in the model to estimate neighborhood-specific percentages of cars, trucks, and buses on arterial versus non-arterial streets. The model was validated on 235 streets. Finally, an exposure-response relationship was used to predict the prevalence of high annoyance for different neighborhoods. Results: Urban noise was found to increase 6.7 dB (p < 0.001) with 10-fold increased street traffic, with important contributors to noise being bus and heavy truck traffic. Living along arterial streets also increased risk of annoyance by 40%. The relative risk of annoyance in one of the City's fastest growing neighborhoods, the South of Market Area, was found to be 2.1 times that of lowest noise neighborhood. However, higher densities of exposed individuals were found in Chinatown and Downtown/Civic Center. Overall, we estimated that 17% of the city's population was at risk of high annoyance from traffic noise. Conclusion: The risk of annoyance from urban noise is large, and varies considerably between neighborhoods. Such risk should be considered in urban areas undergoing rapid growth. We present a relatively simple GIS-based noise model that may be used for routinely evaluating the health impacts of environmental noise. PMID:17584947

Software Tools to Support Research on Airport Departure Planning

NASA Technical Reports Server (NTRS)

Carr, Francis; Evans, Antony; Feron, Eric; Clarke, John-Paul

2003-01-01

A simple, portable and useful collection of software tools has been developed for the analysis of airport surface traffic. The tools are based on a flexible and robust traffic-flow model, and include calibration, validation and simulation functionality for this model. Several different interfaces have been developed to help promote usage of these tools, including a portable Matlab(TM) implementation of the basic algorithms; a web-based interface which provides online access to automated analyses of airport traffic based on a database of real-world operations data which covers over 250 U.S. airports over a 5-year period; and an interactive simulation-based tool currently in use as part of a college-level educational module. More advanced applications for airport departure traffic include taxi-time prediction and evaluation of "windowing" congestion control.

Measurements and modelling of base station power consumption under real traffic loads.

PubMed

Lorincz, Josip; Garma, Tonko; Petrovic, Goran

2012-01-01

Base stations represent the main contributor to the energy consumption of a mobile cellular network. Since traffic load in mobile networks significantly varies during a working or weekend day, it is important to quantify the influence of these variations on the base station power consumption. Therefore, this paper investigates changes in the instantaneous power consumption of GSM (Global System for Mobile Communications) and UMTS (Universal Mobile Telecommunications System) base stations according to their respective traffic load. The real data in terms of the power consumption and traffic load have been obtained from continuous measurements performed on a fully operated base station site. Measurements show the existence of a direct relationship between base station traffic load and power consumption. According to this relationship, we develop a linear power consumption model for base stations of both technologies. This paper also gives an overview of the most important concepts which are being proposed to make cellular networks more energy-efficient.

Symmetry breaking in optimal timing of traffic signals on an idealized two-way street.

PubMed

Panaggio, Mark J; Ottino-Löffler, Bertand J; Hu, Peiguang; Abrams, Daniel M

2013-09-01

Simple physical models based on fluid mechanics have long been used to understand the flow of vehicular traffic on freeways; analytically tractable models of flow on an urban grid, however, have not been as extensively explored. In an ideal world, traffic signals would be timed such that consecutive lights turned green just as vehicles arrived, eliminating the need to stop at each block. Unfortunately, this "green-wave" scenario is generally unworkable due to frustration imposed by competing demands of traffic moving in different directions. Until now this has typically been resolved by numerical simulation and optimization. Here, we develop a theory for the flow in an idealized system consisting of a long two-way road with periodic intersections. We show that optimal signal timing can be understood analytically and that there are counterintuitive asymmetric solutions to this signal coordination problem. We further explore how these theoretical solutions degrade as traffic conditions vary and automotive density increases.

Effect of interactions between vehicles and pedestrians on fuel consumption and emissions

NASA Astrophysics Data System (ADS)

Li, Xiang; Sun, Jian-Qiao

2014-12-01

This paper presents a study of variations of fuel consumption and emissions of vehicles due to random street crossings of pedestrians. The pedestrian and vehicle movement models as well as the interaction model between the two entities are presented. Extensive numerical simulations of single and multiple cars are carried out to investigate the traffic flow rate, vehicle average speed, fuel consumption, CO, HC and NOx emissions. Generally more noncompliant road-crossings of pedestrians lead to higher level of fuel consumptions and emissions of vehicles, and the traffic situation can be improved by imposing higher vehicle speed limit to some extent. Different traffic characteristics in low and high vehicle density regions are studied. The traffic flow is more influenced by crossing pedestrians in the low vehicle density region, while in the high vehicle density region, the interactions among vehicles dominate. The main contribution of this paper lies in the qualitative analysis of the impact of the interactions between pedestrians and vehicles on the traffic, its energy economy and emissions.

Symmetry breaking in optimal timing of traffic signals on an idealized two-way street

NASA Astrophysics Data System (ADS)

Panaggio, Mark J.; Ottino-Löffler, Bertand J.; Hu, Peiguang; Abrams, Daniel M.

2013-09-01

Simple physical models based on fluid mechanics have long been used to understand the flow of vehicular traffic on freeways; analytically tractable models of flow on an urban grid, however, have not been as extensively explored. In an ideal world, traffic signals would be timed such that consecutive lights turned green just as vehicles arrived, eliminating the need to stop at each block. Unfortunately, this “green-wave” scenario is generally unworkable due to frustration imposed by competing demands of traffic moving in different directions. Until now this has typically been resolved by numerical simulation and optimization. Here, we develop a theory for the flow in an idealized system consisting of a long two-way road with periodic intersections. We show that optimal signal timing can be understood analytically and that there are counterintuitive asymmetric solutions to this signal coordination problem. We further explore how these theoretical solutions degrade as traffic conditions vary and automotive density increases.

Measurements and Modelling of Base Station Power Consumption under Real Traffic Loads †

PubMed Central

Lorincz, Josip; Garma, Tonko; Petrovic, Goran

2012-01-01

Base stations represent the main contributor to the energy consumption of a mobile cellular network. Since traffic load in mobile networks significantly varies during a working or weekend day, it is important to quantify the influence of these variations on the base station power consumption. Therefore, this paper investigates changes in the instantaneous power consumption of GSM (Global System for Mobile Communications) and UMTS (Universal Mobile Telecommunications System) base stations according to their respective traffic load. The real data in terms of the power consumption and traffic load have been obtained from continuous measurements performed on a fully operated base station site. Measurements show the existence of a direct relationship between base station traffic load and power consumption. According to this relationship, we develop a linear power consumption model for base stations of both technologies. This paper also gives an overview of the most important concepts which are being proposed to make cellular networks more energy-efficient. PMID:22666026

Weighted complex network analysis of the Beijing subway system: Train and passenger flows

NASA Astrophysics Data System (ADS)

Feng, Jia; Li, Xiamiao; Mao, Baohua; Xu, Qi; Bai, Yun

2017-05-01

In recent years, complex network theory has become an important approach to the study of the structure and dynamics of traffic networks. However, because traffic data is difficult to collect, previous studies have usually focused on the physical topology of subway systems, whereas few studies have considered the characteristics of traffic flows through the network. Therefore, in this paper, we present a multi-layer model to analyze traffic flow patterns in subway networks, based on trip data and an operation timetable obtained from the Beijing Subway System. We characterize the patterns in terms of the spatiotemporal flow size distributions of both the train flow network and the passenger flow network. In addition, we describe the essential interactions between these two networks based on statistical analyses. The results of this study suggest that layered models of transportation systems can elucidate fundamental differences between the coexisting traffic flows and can also clarify the mechanism that causes these differences.

Spatial Analysis of Urban Form and Pedestrian Exposure to Traffic Noise

PubMed Central

Sheng, Ni; Tang, U. Wa

2011-01-01

In the Macao Peninsula, the high population density (49,763 inhabitants/km2) and the lack of control over the number of vehicles (460 vehicles/km) have led to an increase in urban pollution. To provide useful information to local government and urban planners, this paper investigates the spatial distribution of traffic noise in the Macao Peninsula. The interactions among urban form, traffic flow and traffic noise are addressed. Considering the spatial nature of urban geometry and traffic, a high-resolution GIS-based traffic noise model system is applied. Results indicate that the Macao Peninsula has fallen into a situation of serious traffic noise pollution. About 60% of traffic noise levels along the major pedestrian sidewalks in the evening peak hour exceed the National Standard of 70 dB(A) in China. In particular, about 21% of traffic noise levels along the pedestrian sidewalks are above the National Standard by 5 dB(A). Noticeably, the high pedestrian exposure to traffic noise in the historical urban area reduces the comfort of tourists walking in the historic centre and is ruining the reputation of the area as a World Cultural Heritage site. PMID:21776213

Spatial analysis of urban form and pedestrian exposure to traffic noise.

PubMed

Sheng, Ni; Tang, U Wa

2011-06-01

In the Macao Peninsula, the high population density (49,763 inhabitants/km2) and the lack of control over the number of vehicles (460 vehicles/km) have led to an increase in urban pollution. To provide useful information to local government and urban planners, this paper investigates the spatial distribution of traffic noise in the Macao Peninsula. The interactions among urban form, traffic flow and traffic noise are addressed. Considering the spatial nature of urban geometry and traffic, a high-resolution GIS-based traffic noise model system is applied. Results indicate that the Macao Peninsula has fallen into a situation of serious traffic noise pollution. About 60% of traffic noise levels along the major pedestrian sidewalks in the evening peak hour exceed the National Standard of 70 dB(A) in China. In particular, about 21% of traffic noise levels along the pedestrian sidewalks are above the National Standard by 5 dB(A). Noticeably, the high pedestrian exposure to traffic noise in the historical urban area reduces the comfort of tourists walking in the historic centre and is ruining the reputation of the area as a World Cultural Heritage site.

A Wavelet Analysis Approach for Categorizing Air Traffic Behavior

NASA Technical Reports Server (NTRS)

Drew, Michael; Sheth, Kapil

2015-01-01

In this paper two frequency domain techniques are applied to air traffic analysis. The Continuous Wavelet Transform (CWT), like the Fourier Transform, is shown to identify changes in historical traffic patterns caused by Traffic Management Initiatives (TMIs) and weather with the added benefit of detecting when in time those changes take place. Next, with the expectation that it could detect anomalies in the network and indicate the extent to which they affect traffic flows, the Spectral Graph Wavelet Transform (SGWT) is applied to a center based graph model of air traffic. When applied to simulations based on historical flight plans, it identified the traffic flows between centers that have the greatest impact on either neighboring flows, or flows between centers many centers away. Like the CWT, however, it can be difficult to interpret SGWT results and relate them to simulations where major TMIs are implemented, and more research may be warranted in this area. These frequency analysis techniques can detect off-nominal air traffic behavior, but due to the nature of air traffic time series data, so far they prove difficult to apply in a way that provides significant insight or specific identification of traffic patterns.

Self-Organized Criticality and Scaling in Lifetime of Traffic Jams

NASA Astrophysics Data System (ADS)

Nagatani, Takashi

1995-01-01

The deterministic cellular automaton 184 (the one-dimensional asymmetric simple-exclusion model with parallel dynamics) is extended to take into account injection or extraction of particles. The model presents the traffic flow on a highway with inflow or outflow of cars.Introducing injection or extraction of particles into the asymmetric simple-exclusion model drives the system asymptotically into a steady state exhibiting a self-organized criticality. The typical lifetime of traffic jams scales as \\cong Lν with ν=0.65±0.04. It is shown that the cumulative distribution Nm (L) of lifetimes satisfies the finite-size scaling form Nm (L) \\cong L-1 f(m/Lν).

Scheduling Algorithm for Mission Planning and Logistics Evaluation (SAMPLE). Volume 3: The GREEDY algorithm

NASA Technical Reports Server (NTRS)

Dupnick, E.; Wiggins, D.

1980-01-01

The functional specifications, functional design and flow, and the program logic of the GREEDY computer program are described. The GREEDY program is a submodule of the Scheduling Algorithm for Mission Planning and Logistics Evaluation (SAMPLE) program and has been designed as a continuation of the shuttle Mission Payloads (MPLS) program. The MPLS uses input payload data to form a set of feasible payload combinations; from these, GREEDY selects a subset of combinations (a traffic model) so all payloads can be included without redundancy. The program also provides the user a tutorial option so that he can choose an alternate traffic model in case a particular traffic model is unacceptable.

Simulating and evaluating an adaptive and integrated traffic lights control system for smart city application

NASA Astrophysics Data System (ADS)

Djuana, E.; Rahardjo, K.; Gozali, F.; Tan, S.; Rambung, R.; Adrian, D.

2018-01-01

A city could be categorized as a smart city when the information technology has been developed to the point that the administration could sense, understand, and control every resource to serve its people and sustain the development of the city. One of the smart city aspects is transportation and traffic management. This paper presents a research project to design an adaptive traffic lights control system as a part of the smart system for optimizing road utilization and reducing congestion. Research problems presented include: (1) Congestion in one direction toward an intersection due to dynamic traffic condition from time to time during the day, while the timing cycles in traffic lights system are mostly static; (2) No timing synchronization among traffic lights in adjacent intersections that is causing unsteady flows; (3) Difficulties in traffic condition monitoring on the intersection and the lack of facility for remotely controlling traffic lights. In this research, a simulator has been built to model the adaptivity and integration among different traffic lights controllers in adjacent intersections, and a case study consisting of three sets of intersections along Jalan K. H. Hasyim Ashari has been simulated. It can be concluded that timing slots synchronization among traffic lights is crucial for maintaining a steady traffic flow.

Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data.

PubMed

Chen, Feng; Chen, Suren; Ma, Xiaoxiang

2018-06-01

Driving environment, including road surface conditions and traffic states, often changes over time and influences crash probability considerably. It becomes stretched for traditional crash frequency models developed in large temporal scales to capture the time-varying characteristics of these factors, which may cause substantial loss of critical driving environmental information on crash prediction. Crash prediction models with refined temporal data (hourly records) are developed to characterize the time-varying nature of these contributing factors. Unbalanced panel data mixed logit models are developed to analyze hourly crash likelihood of highway segments. The refined temporal driving environmental data, including road surface and traffic condition, obtained from the Road Weather Information System (RWIS), are incorporated into the models. Model estimation results indicate that the traffic speed, traffic volume, curvature and chemically wet road surface indicator are better modeled as random parameters. The estimation results of the mixed logit models based on unbalanced panel data show that there are a number of factors related to crash likelihood on I-25. Specifically, weekend indicator, November indicator, low speed limit and long remaining service life of rutting indicator are found to increase crash likelihood, while 5-am indicator and number of merging ramps per lane per mile are found to decrease crash likelihood. The study underscores and confirms the unique and significant impacts on crash imposed by the real-time weather, road surface, and traffic conditions. With the unbalanced panel data structure, the rich information from real-time driving environmental big data can be well incorporated. Copyright © 2018 National Safety Council and Elsevier Ltd. All rights reserved.

Perception for Outdoor Navigation

DTIC Science & Technology

1991-12-01

are theories of human cognitive activity during driving. Van der Molen and Botticher recently reviewed several of these models [40]. The models...represent driving knowledge, how to perceive traffic situations, or how to process information to obtain actions. Van der Molen and Botticher attempted to...Conference on Robotics and Automation. IEEE, 1987. [40] van der Molen , H.H., and Botticher, A.M.T. Risk Models for Traffic Participants: A Concerted

Exposure to long-term air pollution and road traffic noise in relation to cholesterol: A cross-sectional study.

PubMed

Sørensen, Mette; Hjortebjerg, Dorrit; Eriksen, Kirsten T; Ketzel, Matthias; Tjønneland, Anne; Overvad, Kim; Raaschou-Nielsen, Ole

2015-12-01

Exposure to traffic noise and air pollution have both been associated with cardiovascular disease, though the mechanisms behind are not yet clear. We aimed to investigate whether the two exposures were associated with levels of cholesterol in a cross-sectional design. In 1993–1997, 39,863 participants aged 50–64 year and living in the Greater Copenhagen area were enrolled in a population-based cohort study. For each participant, non-fasting total cholesterol was determined in whole blood samples on the day of enrolment. Residential addresses 5-years preceding enrolment were identified in a national register and road traffic noise (Lden) were modeled for all addresses. For air pollution, nitrogen dioxide (NO2) was modeled at all addresses using a dispersion model and PM2.5 was modeled at all enrolment addresses using a land-use regression model. Analyses were done using linear regression with adjustment for potential confounders as well as mutual adjustment for the three exposures. Baseline residential exposure to the interquartile range of road traffic noise,NO2 and PM2.5 was associated with a 0.58 mg/dl (95% confidence interval: −0.09; 1.25), a 0.68 mg/dl (0.22; 1.16) and a 0.78 mg/dl (0.22; 1.34) higher level of total cholesterol in single pollutant models, respectively. In two pollutant models with adjustment for noise in air pollution models and vice versa, the association between air pollution and cholesterol remained for both air pollution variables (NO2: 0.72 (0.11; 1.34); PM2.5: 0.70 (0.12; 1.28) mg/dl), whereas there was no association for noise (−0.08mg/dl). In three-pollutant models (NO2, PM2.5 and road traffic noise), estimates for NO2 and PM2.5 were slightly diminished (NO2: 0.58 (−0.05; 1.22); PM2.5: 0.57 (−0.02; 1.17) mg/dl). Air pollution and possibly also road traffic noise may be associated with slightly higher levels of cholesterol, though associations for the two exposures were difficult to separate.

Computer-Aided Air-Traffic Control In The Terminal Area

NASA Technical Reports Server (NTRS)

Erzberger, Heinz

1995-01-01

Developmental computer-aided system for automated management and control of arrival traffic at large airport includes three integrated subsystems. One subsystem, called Traffic Management Advisor, another subsystem, called Descent Advisor, and third subsystem, called Final Approach Spacing Tool. Data base that includes current wind measurements and mathematical models of performances of types of aircraft contributes to effective operation of system.

Building the vision, a series of AZTech ITS model deployment success stories for the Phoenix metropolitan area : number five : a strong signal transmitting traffic information via FM subcarrier

DOT National Transportation Integrated Search

1998-01-01

A key element of AZTech's mission is to make up-to-the-minute traffic information available to virtually any traveler. In pursuit of this goal, AZTech set its sights on obtaining an FM subcarrier that could : transmit a wide variety of traffic-relate...