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Online ride-hailing regulation: a simulation study based on evolutionary game theory

Zuo, Wenming; Qiu, Xinxin; Li, Shixin; He, Xinming

Authors

Wenming Zuo

Xinxin Qiu

Shixin Li



Abstract

Game theory contributes to the quantitative study of online ride-hailing regulations; however, prior game models of the online ride-hailing market fail to comprehensively consider government regulation strategies as well as multiple stakeholders in various regulation contexts. This study constructs two system dynamic models of evolutionary games among online ride-hailing platforms, drivers, and passengers. One is the basic model not subject to government regulations, while the other considers government regulations systematically regarding penalty policy, incentive policy, policy adaptability, and public participation. By solving and simulating the model, we study evolutionary stable strategies to control fluctuations in the game process. The results show that an unregulated online ride-hailing system is volatile, and government regulations help stabilize the system. The effect of government regulations can be optimized by adopting a dynamic penalty with a greater initial force, considering platforms as agents in incentive policy, improving policy adaptability, and rewarding public participation.

Journal Article Type Article
Acceptance Date Apr 14, 2023
Online Publication Date Apr 30, 2023
Publication Date 2023
Deposit Date May 5, 2023
Publicly Available Date Oct 31, 2024
Journal Transportation Planning and Technology
Print ISSN 0308-1060
Electronic ISSN 1029-0354
Publisher Taylor and Francis Group
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1080/03081060.2023.2205399
Public URL https://durham-repository.worktribe.com/output/1173382