Multi-Modal Energy Consumption Modeling and Eco-Routing System Development

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Multi-Modal Energy Consumption Modeling and Eco-Routing System Development Multi-modal Energy Consumption Modeling and Eco-routing System Development Jinghui Wang Dissertation submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Civil Engineering Hesham A. Rakha, Chair Linsey C. Marr Pamela Murray-Tuite Ihab El-Shawarby June 8, 2017 Blacksburg, Virginia Keywords: Multi-modal, Eco-routing, Energy Consumption, Greenhouse Gas Emissions, Vehicle Specific Power, Intelligent Transportation System, Advanced Traveler Information System Copyright©2017, Jinghui Wang Multi-modal Energy Consumption Modeling and Eco-routing System Development Jinghui Wang ABSTRACT 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). Specif- ically, 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 Simula- tor (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/de- celeration 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 disser- tation investigates four state-of-the-art microsimulation models (INTEGRATION, VISSIM, AIMSUM, PARAMICS). Given that the car-following model within a micro-simulator con- trols 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 consump- tion 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 reason- able 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 tested congestion levels. The results also demon- strate 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 ratio- nality 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 sig- nificantly 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. Multi-modal Energy Consumption Modeling and Eco-routing System Development Jinghui Wang GENERAL AUDIENCE ABSTRACT 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 consump- tion 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 dis- sertation investigates four state-of-the-art microsimulation models (INTEGRATION, VIS- SIM, AIMSUM, PARAMICS). The results demonstrate that INTEGRATION outperforms the other three simulation models for energy and environmental analysis. Also, INTEGRA- TION 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 environ- J. Wang ment. The
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