Date of Completion
Congestion Pricing, Transportation Network System Improvement, E-hailing Service, Autonomous Vehicle
Dr. Nicholas E. Lownes
Dr. Ling Huang
Dr. Fei Miao
Dr. Karthik Konduri
Dr. Amy Burnicki
Field of Study
Doctor of Philosophy
App-based e-hailing ride services, which connect both drivers and riders through their GPS-enabled mobile devices, have rapidly developed into a viable transportation alternative for many travelers. This technology has changed the set of choices for travelers and has shifted travel patterns, most significantly away from traditional taxi services. These applications provide convenience and flexibility to both drivers and passengers, however, they have also caused several issues. For example, they have pulled ridership off public transportation, damaged the taxi industry, and created new congestion etc. during their expansion. In this research, we analyze available information to arrive at a solution to these issues.
As Autonomous Vehicles (AVs) become possible for E-hailing services to operate, especially when telecom companies start deploying next-generation wireless networks (known as 5G), massive, previously nonexistent transportation information will become available, making it possible for the first time in history to run smart traffic micro-management in real-time. In the first part of this dissertation, a link-based surcharge which considers both the effects of the travelers’ route choices and travel demand patterns is proposed. In this approach, we assume that all links can be surcharged for those using e-haling services, and a heuristic process is applied to address this computationally difficult problem. Meanwhile a cost inverse function is introduced to update the demand changes along paths with different rates of E-haling surcharges. In the second part of this dissertation, working from the assumption that all the E-hailing service vehicles are running by AVs, a link-based dynamic pricing model is proposed to improve road network system and travel time reliability at the same time. In this approach, we assume all AVs will be perfectly informed with updated traffic conditions and will follow dynamic road pricing. In the final part of the dissertation, a density-based surcharge system is proposed to solve the problem of the tragedy of the commons in road congestion. Under this surcharge system, drivers will receive penalties if they choose to take the more congested route and incentives if they choose a less congested route. As a result, the path choices for all drivers will be optimized throughout the whole transportation network. We use the “least-iteration-cycle” approach to lower this computational burden. By iteratively repeating simulations of drivers’ route choices, the system reaches an optimal real-time surcharge framework that significantly reduces congestion.
Wang, Qixing, "E-hailing Pricing and Strategies for Transportation System Performance Improvement" (2019). Doctoral Dissertations. 2391.