Traffic congestion is a one hundred billion dollar problem in the US. The cost of congestion has been trending upward over the last few decades, but has experienced slight decreases in recent years partly due to the impact of congestion reduction strategies. The impact of these strategies is however largely experienced on freeways and not arterials. This discrepancy in impact is partially linked to the lack of real-time, arterial traffic information. Toward this end, this research effort seeks to address the lack of arterial traffic information. To address this dearth of information, this effort developed a methodology to provide accurate estimates of arterial performance measures to transportation facility managers and travelers in real-time. This methodology employs transmitted point sensor data to drive an online, microscopic traffic simulation model. The feasibility of this methodology was examined through a series of experiments that were built upon the successes of the previous, while addressing the necessary limitations. The results from each experiment were encouraging. They successfully demonstrated the method's likely feasibility, and the accuracy with which field estimates of performance measures may be obtained. In addition, the method's results support the viability of a "real-world" implementation of the method. An advanced calibration process was also developed as a means of improving the method's accuracy. This process will in turn serve to inform future calibration efforts as the need for more robust and accurate traffic simulation models are needed. The success of this method provides a template for real-time traffic simulation modeling which is capable of adequately addressing the lack of available arterial traffic information. In providing such information, it is hoped that transportation facility managers and travelers will make more informed decisions regarding more efficient management and usage of the nation's transportation network.

A door-to-door trip may involve multiple traffic modes. For example, travelers may drive to a subway station and make a transfer to rail transit; alternatively, people may also start their trips by walking/cycling to a bus/subway station and then take transit in most of the trip. A successful eco-route planning thus should be able to cover multiple traffic modes and offer intermodal routing suggestions. Developing such a system requires to address extensive concerns. The dissertation is a building block of the multi-modal energy-efficient routing system which is being developed and tested in the simulation environment before real applications. Four submodules have been developed in the dissertation as partial fulfillment of the simulation-based system: energy consumption modeling, subway system development, on-road vehicles dynamic eco-routing, and information effect on route choice behavior. Other submodules such as pedestrian/bicycle modeling will be studied in the future. Towards the research goal, the dissertation first develops fuel consumption models for on-road vehicles. Given that gasoline light duty vehicles (LDVs) and electric vehicles were modeled in previous studies, the research effort mainly focuses on heavy duty vehicles (HDVs). Specifically, heavy duty diesel trucks (HDDTs) as well as diesel and hybrid-electric transit buses are modeled. The models are developed based on the Virginia Tech Comprehensive Power-based Fuel consumption Modeling (VT-CPFM) framework. The results demonstrate that the model estimates are highly consistent with field observations as well as the estimates of the Comprehensive Modal Emissions Model (CMEM) and MOtor Vehicle Emissions Simulator (MOVES). It is also found that the optimum fuel economy cruise speed ranges between 32 and 52 km/h for the tested trucks and between 39 and 47 km/h for the tested buses on grades varying from 0% to 8%, which is significantly lower than LDVs (60-80 km/h). The dissertation then models electric train dynamics and energy consumption in support of subway simulation system development and trip energy estimation. The dynamics model varies throttle and brake level with running speed rather than assuming constants as was done by previous studies, and the energy consumption model considers instantaneous energy regeneration. Both models can be easily calibrated using non-engine data and implemented in simulation systems and eco-transit applications. The results of the dynamics modeling demonstrate that the proposed model can adequately capture instantaneous acceleration/deceleration behavior and thus produce realistic train trajectories. The results of the energy consumption modeling demonstrate that the model produces the estimates consistent with the National Transit Database (NTD) results, and is applicable for project-level analysis given its ability in capturing the energy consumption differences associated with train, route and operational characteristics. The most suitable simulation testbed for system development is then identified. The dissertation investigates four state-of-the-art microsimulation models (INTEGRATION, VISSIM, AIMSUM, PARAMICS). Given that the car-following model within a micro-simulator controls longitudinal vehicle motion and thus determines the resulting vehicle trajectories, the research effort mainly focuses on the performance of the built-in car-following models from the energy and environmental perspective. The vehicle specific power (VSP) distributions resulting from each of the car-following models are compared to the field observations. The results demonstrate that the Rakha-Pasumarthy-Adjerid (RPA) model (implemented in the INTEGRATION software) outperforms the Gipps (AIMSUM), Fritzsche (PARAMICS) and Wiedemann (VISSIM) models in generating accurate VSP distributions and fuel consumption and emission estimates. This demonstrates the advantage of the INTEGRATION model over the other three simulation models for energy and environmental analysis. A new eco-routing model, comprehensively considering microscopic characteristics, is then developed, followed by a numerical experiment to test the benefit of the model. With the resulting eco-routing model, an on-road vehicle dynamic eco-routing system is constructed for in-vehicle navigation applications, and tested for different congestion levels. The results of the study demonstrate that the proposed eco-routing model is able to generate reasonable routing suggestions based on real-time information while at the same time differentiate eco-routes between vehicle models. It is also found that the proposed dynamic eco-routing system achieves lower network-wide energy consumption levels compared to the traditional eco-routing and travel time routing at all congestion levels. The results also demonstrate that the conventional fuel savings relative to the travel time routing decrease with the increasing congestion level; however, the electric power savings do not monotonically vary with congestion level. Furthermore, the energy savings relative to the traditional eco-routing are also not monotonically related to congestion level. In addition, network configuration is demonstrated to significantly affect eco-routing benefits. The dissertation finally investigates the potential to influence driver behavior by studying the impact of information on route choice behavior based on a real world experiment. The results of the experiment demonstrate that the effectiveness of information in routing rationality depends upon the traveler's age, preferences, route characteristics, and information type. Specifically, information effect is less evident for elder travelers. Also, the provided information may not be contributing if travelers value other considerations or one route significantly outperforms the others. The results also demonstrate that, when travelers have limited experiences, strict information is more effective than variability information, and that the faster less reliable route is more attractive than the slower more reliable route; yet the difference becomes insignificant with experiences accumulation. The results of the study will be used to enhance system design through considering route choice incentives. / Ph. D.

Les conditions de circulation sur un réseau routier subissent souvent de la congestion. Selon ses sources, la congestion routière peut être classée en deux catégories : la congestion récurrente déterminée par les lois de trafic et la congestion non-récurrente due aux incidents, accidents ou autres aléas sur la route. Grâce à l'avancement des technologies, notamment en informatique, communication et techniques de traitement des données, l'exploitant est devenu capable de détecter les perturbations, de mesurer les effets et même d'anticiper l'état du trafic afin de mieux adapter ses actions d'exploitation. L'information dynamique concernant les conditions de trafic permet aux usagers de réduire l'inconfort et d'effectuer leur choix d'itinéraire de manière plus raisonnable. Pour l'exploitant, le service d'information aux usagers peut servir à la gestion du trafic. Nous avons étudié la contribution potentielle de l'information dynamique au profit individuel des usagers et à la performance collective du système en prenant en compte : i) la congestion récurrente et non-récurrente ; ii) des différents comportements de choix d'itinéraire en fonction de l'accessibilité à l'information ; iii) d'autres actions de gestion du trafic menées par l'exploitant. Un modèle théorique avec une application analytique sur un réseau élémentaire de deux routes parallèles, une paire origine-destination et deux classes d'usagers respectivement informée ou non-informée nous a permis de retirer de nombreuses indications : i) la diffusion excessive de l'information avec un contenu « neutre » dégrade à la fois le profit individuel et la performance du système ; ii) l'information dynamique avec certain contenu « coopératif » peut contribuer l'optimisation du système sans causer le problème d'acceptabilité ; iii) l'information dynamique et d'autres mesures de gestion dynamique s'interagissent de manière complémentaire à l'optimisation du trafic / Traffic conditions on a road network often suffer from congestion. According to sources, the traffic congestion can be classified into two categories : recurrent congestion determined by the physic laws of traffic and non-recurrent congestion due to incidents, accidents or other hazards on the road. Thanks to the advancement of technologies, including computers, communications and data processing, the traffic operator is now able to detect disturbances, to measure the effects and even to anticipate traffic conditions to better match traffic management activities. Dynamic information on traffic conditions enables users to reduce discomfort and make their route choice decision more reasonable. For the operator, the service user information may be used as a traffic management tool. We investigated the potential contribution of dynamic traffic information for the benefit of individual users and system performance by taking into account : i) recurring congestion and non-recurring ; ii) different route choice behaviours based on accessibility to information service ; iii) other traffic management actions taken by the traffic operator. A theoretical model with an analytical application on a simple two-parallel-road network, an origin-destination pairs and two user classes, respectively-informed or non-informed has given many conclusions : i) an excessive distribution of traffic information with a « neutral » content damages both the individual profit and system performance ; ii) traffic information with some « cooperative » content may help optimize the system performance without causing acceptability problem ; and iii) dynamic information and other traffic management tools interplay in a complementary manner to optimize the traffic

Cette thèse aborde la problématique de l’adoption des systèmes d’information (SI), partant du problème posé par le déficit d’utilisation des applications mobiles d’information voyageur, applications pourtant conçues pour aider à résoudre les difficultés liées aux transports dans les sociétés modernes. Elle tente de répondre à la question de savoir quels facteurs peuvent favoriser l’utilisation des SI par leur public potentiel. Face à la suprématie de l’approche centrée ‘système’, donnant priorité au facteur technologique dans l’identification des déterminants de l’utilisation des SI, ce travail défend le point de vue selon lequel une approche psychosociale est mieux à même de mettre à jour les processus par lesquels les individus en viennent à utiliser ces systèmes. Dans ce cadre, le premier axe de cette recherche interroge le rôle des croyances dans l’adoption. Les résultats obtenus sous cet axe montrent que les modèles basés sur les croyances prédisent assez bien l’intention d’utilisation des SI prise comme indicateur d’adoption. Mais, ils montrent aussi les limites de ces modèles dans la prédiction de l’intention d’utilisation initiale, à l’étape de l’acceptabilité, une étape pré-adoptive stratégique où le seuil de la première expérience d’utilisation n’est pas encore franchi (étude 1). Cela pose la nécessité d’aller au-delà des croyances pour mieux cerner les déterminants de l’intention d’utilisation initiale. A cet effet, le second axe de cette recherche interroge le rôle des attentes dans l’adoption. Les résultats obtenus sous cet axe montrent que les attentes sont des prédicteurs performants des croyances pré-adoptives, et, au-delà, des prédicteurs directs de l’intention d’utilisation initiale (études 2 et 3). Ces résultats sont d’une grande portée théorique et empirique. Ils montrent que les attentes entrent en jeu directement dans l’explication de l’intention d’utilisation initiale, à l’étape pré-adoptive, quand celles-ci ont toujours été considérées comme opérantes uniquement à l’étape post-adoptive où leur infirmation/confirmation à l’issue d’une première expérience d’utilisation détermine le degré de satisfaction et l’intention de continuer l’utilisation. Au total, cette thèse amène un éclairage nouveau sur la façon dont les croyances et les attentes se mettent en scène le long du processus d’adoption, et pèsent de tout leur poids sur les décisions relatives à l’utilisation des SI. / This thesis addresses the problem of information systems (IS) adoption, starting from the problem posed by the deficit in use of traveler information mobile applications, yet applications designed to help solve problems related to transport in modern societies. It attempts to answer the question of which factors may improve IS use in their potential audience. Given the supremacy of the system-centered approach giving priority to technological factor in identifying the determinants of IS use, this work defends the view that a psychosocial approach is better able to elicit the processes by which individuals come to use these systems. In this context, the first axis of this research examines the role of beliefs in adoption. The results obtained under this axis show that beliefs-based models predict quite well intention to use IS taken as an indicator of adoption. But they also show the limitations of these models in predicting initial use intention, at the acceptability stage, a strategic pre-adoptive stage when the threshold of the first experience of use is not yet crossed (study 1). This raises the need to go beyond beliefs to better understand the determinants of initial use intention. To this end, the second axis of this research questions the role of expectations in adoption. The results obtained under this axis show that expectations are powerful predictors of pre-adoptive beliefs, and, beyond, direct predictors of initial use intention (studies 2 and 3). These results are of great theoretical and empirical significance. They show that expectations are directly involved in the explanation of initial use intention at the pre-adoptive stage, when these have always been considered as operating only in the post-adoptive stage where their disconfirmation/confirmation after a first experience of use determines the degree of satisfaction and continued use intention. Overall, this thesis brings new light on how beliefs and expectations come into play along the adoption process, and weigh fully on decisions related to IS use.

Titre: Simulation multi-agent de l’information des voyageurs dans les transports en commun.Résumé:Avec la généralisation de l'information temps-réel, le comportement des réseaux de transport modernes devient de plus en plus difficile à analyser et à prévoir. Le rôle de l'information est de plus en plus critique, particulièrement en cas de dysfonctionnement des réseaux, et l’information devient de plus en plus personnalisée et individuelle. Plusieurs phénomènes tels que la saturation, la concentration et la sur-réaction peuvent être observés après l’utilisation de systèmes d’information voyageurs. En effet, sans contrôle, la diffusion massive d'informations, à travers les panneaux à messages variables, les annonces dans les médias ainsi que les dispositifs de guidage individuel peut avoir des effets pervers et créer de nouvelles congestions. Ainsi, il est devenu important de développer des outils de simulation pour les décideurs de politiques de mobilité, prenant en compte ce nouvel environnement informationnel.Dans ce travail de thèse, nous proposons une simulation multi-agent pour mesurer l'impact de la fourniture d'informations sur la qualité des voyages en transports en commun, notamment dans des situations perturbées, en prenant en compte un environnement informationnel hétérogène. Dans un premier temps, nous concevons une simulation qui assure le déplacement de voyageurs sur un réseau de transport en commun. Ensuite, nous l’enrichissons par l’intégration de l’information des voyageurs et des taux d’équipements des voyageurs en smartphones, de telle manière qu’il puisse représenter les voyageurs connectés et être capable de distinguer l’impact des informations personnelles de celui des informations générales. Pour ce faire, nous nous fondons sur le paradigme multi-agent, qui est un modèle puissant pour la conception et l'implantation d'applications de transport. Pour répondre aux besoins de l’intégration de l’information des voyageurs, en particulier l’information individuelle, nous adoptons une approche centrée-environnement où l’environnement spatio-temporel multi-agent est l’interlocuteur des agents voyageurs et représente l’évolution dans le temps de l’état du réseau de transport en commun.Afin de tester notre simulateur dans un contexte réaliste de déplacement, nous utilisons les données réelles du réseau de Toulouse. Pour évaluer l’impact de la provision d’information voyageur sur le réseau, nous testons différents scénarios en fonction du pourcentage de voyageurs connectés représentés par des agents. Ces scénarios simulés sont analysés suivant leur impact sur les temps de parcours moyens des voyageurs, connectés et non connectés. Les résultats montrent que le nombre de voyageurs connectés a un impact positif sur les temps de parcours jusqu’à un certain seuil, au delà duquel l’impact devient relativement négatif / Title: Multiagent simulation of traveler information on transit networks.Abstract:With the generalization of real-time traveler information, the behavior of modern transport networks becomes harder to analyze and to predict. Advanced traveler Information systems play a major role in modern transportation system, mainly in case of disturbances, and the information is becoming more personalized and individual. Different phenomena such as over-saturation, concentration and over-reaction can be observed after the use of advanced traveler information systems. In fact, without control, the massive spread of information via billboards, radio announcements and individual guidance may have perverse effects and create new traffic jams. It is now critical to develop simulation tools for mobility policies makers, taking into account this new information environment to observe these effects and to consider the proper methods to deal with them.In this PHD work, we propose a multiagent simulation to measure the impact of information provision on the quality of passengers’ travels, notably in case of disturbances, taking into account a heterogeneous information environment. First, we design and implement a simulation to ensure travelers movement in a transit network. Then, we enrich our model to integrate traveler information system and to represent travelers equipped with smart phones. It allows us to evaluate separately personal and general information. To this end, we use the multi-agent paradigm, which is proven to be a powerful model to design and implement transportation applications. To deal with the integration of the traveler information system in the simulator, we adopt an environment-centered approach, where the space-time multiagent environment is the privileged interlocutor of the agents and represent the evolution of the transit network state over time.To test our simulator in a real context, we use real data on the city of Toulouse, France. To assess the impact of information provision, we simulate different scenarios in function of the percentage of connected travelers, represented as agents. These simulated scenarios are analyzed following their impact on the average travel times of the travelers (connected and no-connected). Results show that the number of connected travelers has a positive impact on overall travel times up until a certain threshold before becoming relatively negative

High speed facilities are considered the backbone of any successful transportation system; Interstates, freeways, and expressways carry the majority of daily trips on the transportation network. Although these types of roads are relatively considered the safest among other types of roads, they still experience many crashes, many of which are severe, which not only affect human lives but also can have tremendous economical and social impacts. These facts signify the necessity of enhancing the safety of these high speed facilities to ensure better and efficient operation. Safety problems could be assessed through several approaches that can help in mitigating the crash risk on long and short term basis. Therefore, the main focus of the research in this dissertation is to provide a framework of risk assessment to promote safety and enhance mobility on freeways and expressways. Multi-level Safety Performance Functions (SPFs) were developed at the aggregate level using historical crash data and the corresponding exposure and risk factors to identify and rank sites with promise (hot-spots). Additionally, SPFs were developed at the disaggregate level utilizing real-time weather data collected from meteorological stations located at the freeway section as well as traffic flow parameters collected from different detection systems such as Automatic Vehicle Identification (AVI) and Remote Traffic Microwave Sensors (RTMS). These disaggregate SPFs can identify real-time risks due to turbulent traffic conditions and their interactions with other risk factors. In this study, two main datasets were obtained from two different regions. Those datasets comprise historical crash data, roadway geometrical characteristics, aggregate weather and traffic parameters as well as real-time weather and traffic data. iii At the aggregate level, Bayesian hierarchical models with spatial and random effects were compared to Poisson models to examine the safety effects of roadway geometrics on crash occurrence along freeway sections that feature mountainous terrain and adverse weather. At the disaggregate level; a main framework of a proactive safety management system using traffic data collected from AVI and RTMS, real-time weather and geometrical characteristics was provided. Different statistical techniques were implemented. These techniques ranged from classical frequentist classification approaches to explain the relationship between an event (crash) occurring at a given time and a set of risk factors in real time to other more advanced models. Bayesian statistics with updating approach to update beliefs about the behavior of the parameter with prior knowledge in order to achieve more reliable estimation was implemented. Also a relatively recent and promising Machine Learning technique (Stochastic Gradient Boosting) was utilized to calibrate several models utilizing different datasets collected from mixed detection systems as well as real-time meteorological stations. The results from this study suggest that both levels of analyses are important, the aggregate level helps in providing good understanding of different safety problems, and developing policies and countermeasures to reduce the number of crashes in total. At the disaggregate level, real-time safety functions help toward more proactive traffic management system that will not only enhance the performance of the high speed facilities and the whole traffic network but also provide safer mobility for people and goods. In general, the proposed multi-level analyses are useful in providing roadway authorities with detailed information on where countermeasures must be implemented and when resources should be devoted. The study also proves that traffic data collected from different detection systems could be a useful asset that should be utilized iv appropriately not only to alleviate traffic congestion but also to mitigate increased safety risks. The overall proposed framework can maximize the benefit of the existing archived data for freeway authorities as well as for road users.

科学研究費補助金 研究種目:基盤研究(B)(2) 課題番号:11450193 研究代表者:森川 高行 研究期間:1999-2001年度