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Market Design for Next Generation of Shared and Electric Transportation Systems: Modeling, Optimization, and Learning

Shao, Shiping
http://orcid.org/0000-0002-0373-1883
http://rave.ohiolink.edu/etdc/view?acc_num=osu1669301423117182

Abstract Details

2022, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
The overarching goal of this thesis is to build an optimized, customer-friendly, efficient, and risk-averse mechanisms to operate the next generation of shared and electric transportation markets. We conduct mathematical modeling, optimization, large-scale simulation, and learning algorithms for three applications in the markets: The first application is routing and pricing mechanism design in ridehailing market. We devises a novel fair pricing policy for the on-demand transportation system under asymmetrical demand patterns. We assume that the city comprises multiple nodes with directed edges connecting the nodes. At each node, passengers arrive according to a Poisson distribution. Based on the arrival processes of the passengers and the operational cost of moving a vehicle from one node to another, we devise a fair pricing scheme for the passengers using the Shapley value. We show that at certain nodes, as the arrival rate or the valuation distribution of the passengers increases, the fair pricing policy increases up to a threshold, and then decreases after a different threshold. Our numerical simulations illustrate the theoretical findings. The second application is the scheduling optimization in a largely scaled EV charging market. We develop a new algorithm for scheduling the charging process of a large number of electric vehicles (EVs) over a finite horizon. We assume that EVs arrive at the charging stations with different charge levels and different flexibility windows. The arrival process is assumed to have a known distribution and that the charging process of EVs can be preemptive. We pose the scheduling problem as a dynamic program with constraints. We show that the resulting formulation leads to a monotone dynamic program with Lipschitz continuous value functions that are robust against perturbation of system parameters. We propose a simulation based fitted value iteration algorithm to determine the value function approximately, and derive the sample complexity for computing the approximately optimal solution. We further apply the scheduling algorithm to efficiently schedule the charging processes of a large number of electric vehicles (EVs) with flexibility, which allows the EV charging service provider to charge the EVs between their minimum and target state of charge (SoC) within a time window. We propose a two-stage decoupled algorithm to reduce the dimensions in the fitted value iteration to reduce the computation complexity, and provide sufficient conditions for achieving optimality. The third application considers risk-sensitive Markov decision processes (MDPs), where the MDP model is influenced by a parameter which takes values in a compact metric space. We identify sufficient conditions under which small perturbations in the model parameters lead to small changes in the optimal value function and optimal policy. We further establish the robustness of the risk-sensitive optimal policies to modeling errors. Implications of the results for data-driven decision-making, decision-making with preference uncertainty, and systems with changing noise distribution are discussed.
Abhishek Gupta (Advisor)
Yingbin Liang (Committee Member)
Candice Askwith (Committee Member)
Cathy Xia (Committee Member)
Parinaz Naghizadeh (Committee Member)
175 p.
Electrical Engineering
Market Design, Ride-hailing System, EV Charging, Risk Analysis

Recommended Citations

Citations

  • Shao, S. (2022). Market Design for Next Generation of Shared and Electric Transportation Systems: Modeling, Optimization, and Learning [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1669301423117182

    APA Style (7th edition)

  • Shao, Shiping. Market Design for Next Generation of Shared and Electric Transportation Systems: Modeling, Optimization, and Learning . 2022. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1669301423117182.

    MLA Style (8th edition)

  • Shao, Shiping. "Market Design for Next Generation of Shared and Electric Transportation Systems: Modeling, Optimization, and Learning ." Doctoral dissertation, Ohio State University, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=osu1669301423117182

    Chicago Manual of Style (17th edition)

osu1669301423117182
154
© 2022, some rights reserved.Creative Commons License Market Design for Next Generation of Shared and Electric Transportation Systems: Modeling, Optimization, and Learning by Shiping Shao is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by The Ohio State University and OhioLINK.