The accurate and reliable estimation of travel time based on point detector data is needed to support Intelligent Transportation System (ITS) applications. It has been found that the quality of travel time estimation is a function of the method used in the estimation and varies for different traffic conditions. In this study, two hybrid on-line travel time estimation models, and their corresponding off-line methods, were developed to achieve better estimation performance under various traffic conditions, including recurrent congestion and incidents. The first model combines the Mid-Point method, which is a speed-based method, with a traffic flow-based method. The second model integrates two speed-based methods: the Mid-Point method and the Minimum Speed method. In both models, the switch between travel time estimation methods is based on the congestion level and queue status automatically identified by clustering analysis. During incident conditions with rapidly changing queue lengths, shock wave analysis-based refinements are applied for on-line estimation to capture the fast queue propagation and recovery. Travel time estimates obtained from existing speed-based methods, traffic flow-based methods, and the models developed were tested using both simulation and real-world data. The results indicate that all tested methods performed at an acceptable level during periods of low congestion. However, their performances vary with an increase in congestion. Comparisons with other estimation methods also show that the developed hybrid models perform well in all cases. Further comparisons between the on-line and off-line travel time estimation methods reveal that off-line methods perform significantly better only during fast-changing congested conditions, such as during incidents. The impacts of major influential factors on the performance of travel time estimation, including data preprocessing procedures, detector errors, detector spacing, frequency of travel time updates to traveler information devices, travel time link length, and posted travel time range, were investigated in this study. The results show that these factors have more significant impacts on the estimation accuracy and reliability under congested conditions than during uncongested conditions. For the incident conditions, the estimation quality improves with the use of a short rolling period for data smoothing, more accurate detector data, and frequent travel time updates.

Presently, the United States has yet to achieve the 1991 Intermodal Surface Transportation Efficiency Act's (ISTEA) goal of creating a seamless intermodal transportation system. In addition to the dearth of connections, the nation's poor transportation information systems limit intercity intermodal transportation. Travelers lack awareness of available transportation options and face too many separate portals for trip planning that both consume time and present inadequate information. This paper posits that the creation of an efficient and extensive web-based door-to-door intermodal trip planner can solve these problems. The proposed system will present travelers with a single portal to meet all their trip planning needs. Upon selecting specific trips, travelers can then decide to be directed to operators to make a purchase. The system will include operators from the major modal groups including intercity buses, intercity rail, commuter rail, transit, and airlines. It will also include taxis due to the disjointed nature of the US public transportation system and the need to connect users who are far from stations. The requirements to create this trip planner are explored, including the support systems, potential legal issues, and suitable entities for administration and management. A survey of 39 transportation system users revealed the existence of redundant and inadequate trip planners and that the lack of sufficient information on public transportation options is driving travelers to private vehicles for shorter distances even for those who prefer public means of transportation. Analysis of the costs and benefits of implementing the proposed system is drawn from interviews with key personnel within the transportation industry, and a review of nationwide trip planners in European countries. Finally, a roadmap is presented on how best to implement the system with inputs from both the public and private sector. Recommendations include the establishment of an industry-wide data standard, a national interagency database, and a cooperative structure that entices major players within each mode to participate in the system. Also suggested are incentives from the DOT and interested private sector members to encourage more operators to participate in the system. / Master of Science

Traveler information systems play a significant role in most travelers’ daily trips. These systems assist travelers in choosing the best routes to reach their destinations and possibly select suitable departure times and modes for their trips. Connected Vehicle (CV) technologies are now in the pilot program stage. Vehicle-to-Infrastructure (V2I) communications will be an important source of data for traffic agencies. If this data is processed properly, then agencies will be able to better determine traffic conditions, allowing them to take proper countermeasures to remedy transportation system problems under different conditions. This research focuses on developing methods to assess the potential of utilizing CV data to support the traveler information system data collection process. The results from the assessment can be used to establish a timeline indicating when an agency can stop investing, at least partially, in traditional technologies, and instead rely on CV technologies for traveler information system support. This research utilizes real-world vehicle trajectory data collected under the Next Generation Simulation (NGSIM) program and simulation modeling to emulate the use of connected vehicle data to support the traveler information system. NGSIM datasets collected from an arterial segment and a freeway segment are used in this research. Microscopic simulation modeling is also used to generate required trajectory data, allowing further analysis, which is not possible using NGSIM data. The first step is to predict the market penetration of connected vehicles in future years. This estimated market penetration is then used for the evaluation of the effectiveness of CV-based data for travel time and volume estimation, which are two important inputs for the traveler information system. The travel times are estimated at different market penetrations of CV. The quality of the estimation is assessed by investigating the accuracy and reliability with different CV deployment scenarios. The quality of volume estimates is also assessed using the same data with different future scenarios of CV deployment and partial or no detector data. Such assessment supports the identification of a timeline indicating when CV data can be used to support the traveler information system.

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.

This research develops and evaluates an Advanced Traveler Information System (ATIS) model using a Vehicle-to-Vehicle (V2V) communication system (referred to as the GATIS-V2V model) with the off-the-shelf microscopic simulation model, VISSIM. The GATIS-V2V model is tested on notional small traffic networks (non-signalized and signalized) and a 6X6 typical urban grid network (signalized traffic network). The GATIS-V2V model consists of three key modules: vehicle communication, on-board travel time database management, and a Dynamic Route Guidance System (DRGS). In addition, the system performance has been enhanced by applying three complementary functions: Autonomous Automatic Incident Detection (AAID), a minimum sample size algorithm, and a simple driver behavior model. To select appropriate parameter ranges for the complementary functions a sensitivity analysis has been conducted. The GATIS-V2V performance has been investigated relative to three underlying system parameters: traffic flow, communication radio range, and penetration ratio of participating vehicles. Lastly, the enhanced GATIS-V2V model is compared with the centralized traffic information system. This research found that the enhanced GATIS-V2V model outperforms the basic model in terms of travel time savings and produces more consistent and robust system output under non-recurrent traffic states (i.e., traffic incident) in the simple traffic network. This research also identified that the traffic incident detection time and driver's route choice rule are the most crucial factors influencing the system performance. As expected, as traffic flow and penetration ratio increase, the system becomes more efficient, with non-participating vehicles also benefiting from the re-routing of participating vehicles. The communication radio ranges considered were found not to significantly influence system operations in the studied traffic network. Finally, it is found that the decentralized GATIS-V2V model has similar performance to the centralized model even under low flow, short radio range, and low penetration ratio cases. This implies that a dynamic infrastructure-based traffic information system could replace a fixed infrastructure-based traffic information system, allowing for considerable savings in fixed costs and ready expansion of the system off of the main network corridors.

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

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