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Transportation-related risk factors are a major source of morbidity and mortality in China, where the expansion of road networks and surges in personal vehicle ownership are having profound effects on public health. Road traffic injuries and fatalities have increased alongside increased use of motorised transport in China, and accident injury risk is aggravated by inadequate emergency response systems and trauma care. National air quality standards and emission control technologies are having a positive effect on air quality, but persistent air pollution is increasingly attributable to a growing and outdated vehicle fleet and to famously congested roads. Urban design favours motorised transport, and physical activity and its associated health benefits are hindered by poor urban infrastructure. Transport emissions of greenhouse gases contribute substantially to regional and global climate change, which compound public health risks from multiple factors. Despite these complex challenges, technological advances and innovations in planning and policy stand to make China a leader in sustainable, healthy transportation.

The study aims to develop a novel evolution model of speed perturbations for quantifying the probability of the traffic flow breakdown on congested freeway flow. The model is applicable to freeway operation and management. Time headway, driver behavior, and state transitions with stochastic nature are the most important factors for the breakdown and recovery of a disturbance. Two types of drivers, 'adventurers' and 'conservatives,' and two styles of reacting behavior, 'copy' and 'shift' are defined to characterize driving behavior. The time headway distributions for arriving and discharging traffic are distinguished for the existence of the 'hysteresis' phenomenon. The Monte Carlo simulation is carried out to obtain the estimation of the probability of breakdown (PB). The calculated PB is a monotonically increasing function of freeway flow with an 'S' shape. The simulation results indicate that the proposed model can describe the changes in spatial extent and magnitude of a disturbance, which have potential use for future works and field applications. Key words: traffic breakdown, stochastic nature, time headway, driving behavior, car-following theory. Cette etude vise a developper un modele innovateur devolution des perturbations de vitesse afin de quantifier la probability de distribution de la circulation sur une autoroute congestionnee. Le modele peut etre utilise pour l'operation et la gestion des autoroutes. L'intervalle entre les passages, le comportement des conducteurs et la transition d'etats de nature stochastique sont les plus importants facteurs a considerer pour la distribution de la circulation et la recuperation a la suite d'une perturbation. Deux types de conducteurs, les << aventuriers >> et les << conservateurs >> et deux styles de comportement reactif, la << copie >> et le << deplacement >> sont definis pour caracteriser le comportement de conduite. Les distributions des intervalles entre les passages pour la circulation entrant et sortant ont ete separees pour l'existence du phenomene d'hysterese. Une simulation de Monte-Carlo est effectuee pour estimer la probabilite d'une distribution de la circulation (PB). Le PB calcule est une fonction monotone croissante de la circulation sur l'autoroute presentant la forme d'un << S >>. Les resultats de la simulation indiquent que le modele propose peut decrire les changements dans l'etendue spatiale et l'amplitude d'une perturbation, ce qui represente une utilisation potentielle pour les travaux futurs et les applications sur le terrain. [Traduit par la Redaction] Mots-cles: distribution de la circulation, nature stochastique, intervalle entre les passages, comportement des conducteurs, theorie de la poursuite., Introduction Traffic congestion caused by instability of traffic flow induces travel delays, high risks of accidents, frequent accelerations and decelerations, and an increase of emissions. Once a congestion queue appears, [...]

Providing reliable real-time travel time information is a critical challenge to all existing traffic routing systems. This study develops a new model for estimating and predicting real-time traffic conditions and travel times for variable message signs-based route guidance system. The proposed model is based on real-time limited detected traffic data, stochastic nonlinear macroscopic traffic flow model, and adaptive Kalman filtering theory. The method has the following main features: (1) real-time estimation and prediction of traffic conditions on a network level using limited traffic detectors, (2) travel time prediction in free flow and congested flow, and (3) prediction of drivers' en-route diversion behavior. Field testing is conducted based on the Route Guidance Pilot Project sponsored by the National Science and Technology Ministry of China. The achieved testing results are satisfactory and have potential use for future works and field applications. Key words: travel time prediction, route guidance, adaptive Kalman filtering, macroscopic traffic flow model, driver behavior. Fournir de l'information fiable sur le temps de trajet en temps reel est un defi important pour tous les systemes d' aiguillage du trafic. La presente etude developpe un nouveau modele pour estimer et predire les conditions de trafic et les temps de trajet en temps reel pour un systeme de guidage routier base sur des panneaux de signalisation. Le modele propose est base sur des donnees limitees de trafic detecte en temps reel, un modele stochastique non lineaire et macroscopique de flux du trafic et sur la theorie de filtrage adaptatif de Kalman. La methode possede les caracteristiques principales suivantes: (1) estimation et prevision en temps reel des conditions de trafic au niveau du reseau routier utilisant des detecteurs limites de trafic, (2) la prevision du temps de trajet dans un flux de trafic fluide et un flux congestionne et (3) la prevision, en route, du comportement de diversion des conducteurs. Les essais sur le terrain sont bases sur le projet pilote de guidage routier commandite par le ministere de la Technologie et des Sciences naturelles de la Chine. Les resultats des essais sont satisfaisants et pourraient servir dans de futurs travaux et applications sur le terrain. Mots-cles : prevision du temps de trajet, guidage routier, filtrage adaptatif de Kalman, modele macroscopique de flux de trafic, comportement du conducteur. [Traduit par la Redaction], 1. Introduction A successful and efficient variable message signs (VMS) based route guidance system (RGS) relies on drivers' response to VMS messages (Xu et al. 2011a). Therefore, VMS-based RGS for [...]

Hybrid electric vehicles (HEVs) have demonstrated the capability to improve fuel economy and emissions. The plug-in HEV (PHEV), utilizing more battery power, has become a more attractive upgrade of the HEV. The charge-depletion mode is more appropriate for the power management of PHEVs, i.e., the state of charge (SOC) is expected to drop to a low threshold when the vehicle reaches the trip destination. Trip information has so far been considered as future information for vehicle operation and is thus not available a priori. This situation can be changed by the recent advancement in intelligent transportation systems (ITSs) based on the use of on-board global positioning systems (GPSs), geographical information systems (GISs), and advanced traffic flow modeling techniques. In this paper, a new approach to optimal power management of PHEVs in the charge-depletion mode is proposed with driving cycle modeling based on the historic traffic information. A dynamic programming (DP) algorithm is applied to reinforce the charge-depletion control such that the SOC drops to a specific terminal value at the end of the driving cycle. The vehicle model was based on a hybrid electric sport utility vehicle (SUV). Only fuel consumption is considered for the current stage of the study. A simulation study was conducted for several standard driving cycles and two trip models using the proposed method, and the results showed significant improvement in fuel economy compared with a rule-based control and a depletion sustenance control for most cases. Furthermore, the results showed much better consistency in fuel economy compared with rule-based and depletion sustenance control. Index Terms--Dynamic programming (DP), intelligent transportation systems (ITSs), plug-in hybrid electric vehicles (PHEVs), power management.