Collaborative Research: Modeling and Analysis of Advanced Parking Management for Traffic Congestion Mitigation Collaborative Research: Modeling and Analysis of Advanced Parking Management for Traffic Congestion Mitigation Collaborative Research: Modeling and Analysis of Advanced Parking Management for Traffic Congestion Mitigation We envision the proliferation of smartphone-based advanced parking management services, which may provide information on real-time availability and price of parking spaces, and allow motorists to make reservations and/or guide them to open parking spaces. The proposed research provides analytical foundations and methodologies for analyzing these emerging parking management services to better understand their impacts and implications on parking competition and travel patterns, and shed lights on optimal design of those services to reduce traffic congestion in dense urban districts. The proposed project investigates a variety of theoretical questions associated with the analysis and design of smartphone-based parking management services. We do not attempt to model these services in their full complexity. Instead, we adopt a game-theoretical approach to develop structural models to examine how the spontaneous responses of drivers to those services collectively affect travel patterns and traffic congestion. Our primary intent is to provide insights on optimal design of parking management strategies and bound their efficiency in reducing traffic congestion under simplified macroscopic settings. Our exploration will be conducted in the following three main tasks. The first two aim to gain understanding on how advanced parking services, such as parking availability information, parking search strategies, reservation, and pricing, could affect temporal and spatial travel patterns respectively. The last task develops an agent-based simulation to validate the analytical results obtained from the first two tasks. Our preliminary analysis shows that if multiple differentiated expiration times are properly designed, expirable reservations will help smooth out the peak to reduce the total travel cost. We explore optimal designs of the scheme under different levels of parking provision and then bound their efficiencies in reducing the total travel cost. The first task will extend our preliminary research to consider the impacts of parking search time and the uncertainty related to bottleneck congestion, and then analyze the benefits of parking reservation schemes in those cases. We will further generalize our analysis framework for a single bottleneck to general transportation networks. Moreover, we will investigate how parking reservations affect mode choice and the temporal distribution of demand when considering both morning and evening peaks together. In the second task, we will focus on optimal parking search strategies for individual drivers with given (probabilistic) travel and parking information, drivers strategic interactions in the parking competition, and the outcome of such a non-cooperative game. We will then investigate parking information, reservation and navigation services and pricing policies to shift the outcome of the game to a more socially desirable state with less congestion and fewer emissions. The last task will build an agent-based simulation model to verify the predictions and insights drawn from the proposed theoretical tools, and better understand the system in mathematically intractable yet empirically important cases. The simulation also provides a very useful tool for our education and outreach activities. The principal investigators of this research are Dr. Yafeng Yin at University of Florida (UF) and Dr. Yingyan Lou at Arizona State University (ASU). They will be joined by two PhD students. Dr. Yin will lead the analysis of parking competition and Dr. Lou will take the lead on developing optimal parking navigation strategies and agent-based simulation. The two PIs will collaborate on the education and outreach activities.
|Effective start/end date||9/1/14 → 8/31/18|
- National Science Foundation (NSF): $170,000.00
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