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.

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. / 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) including heavy duty diesel trucks (HDDTs) as well as diesel and hybrid-electric transit buses. The model estimates are demonstrated to provide a good fit to field data. The dissertation then models electric train dynamics and energy consumption in support of subway simulation system development and trip energy estimation. The proposed dynamics model is able to produce realistic acceleration behavior, and the proposed energy consumption model can provide robust energy estimates that are consistent with field data. Both models can be calibrated without mechanical data and thus easily implemented in complex frameworks such as simulation systems and eco-transit applications. 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). The results demonstrate that INTEGRATION outperforms the other three simulation models for energy and environmental analysis. Also, INTEGRATION is able to generate measures of effectiveness (MOEs) for electric vehicles, which makes it more competitive than the state-of-the-art counterpart. A dynamic eco-routing system is then developed in the INTEGRATION simulation environment. The built-in eco-routing model of the system comprehensively considers microscopic characteristics and is demonstrated to generate reasonable routing solutions based on real-time information while at the same time differentiate vehicle models. The system is able to provide routing suggestions for both conventional gasoline/diesel and electric vehicles. The testing results demonstrate that the proposed eco-routing system achieves network-wide energy savings compared to the traditional eco-routing and travel time routing at all tested congestion levels. Also, 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.

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.

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