Optimizing subsidy strategies of the ride-sourcing platform under government regulation. (May 2023)

• Record Type: Journal Article
• Authors: Tang, Wei; Xie, Ningke; Mo, Dong; Cai, Zeen; Lee, Der-Horng; Chen, Xiqun (Michael)
• Is Part Of: Transportation research. Volume 173(2023)
• Publication Date: 2023-05
• Subjects: Ride-sourcing market -- Subsidy and promotion -- Game theory -- Labor supply -- Government regulation; Logistics -- Periodicals; Transportation -- Periodicals; 388.011
• DOI: 10.1016/j.tre.2023.103112
• Languages: English
• ISSNs: 1366-5545

Abstract

Highlights: Propose a multi-stage game-theoretic model to reveal coupling game among stakeholders. Model decisions of heterogeneous passengers/drivers and ride-sourcing platform. Derive endogenous influences of platform subsidy strategies on drivers and passengers. Gain insights into optimizing ride-sourcing platform subsidy strategies under regulation. Conduct numerical experiments to illustrate response curves to platform subsidies. Provide suggestions for the platform and support government decision-making. Abstract: Under governmental regulation of ride-sourcing platforms, subsidies on passengers and drivers have become an effective incentive to coordinate supply and demand. To achieve the optimization goal of both growth and savings, platforms need to know the impact of the two subsidies on coordinating the supply and demand and what amount of the subsidy is appropriate. This paper formulates a multi-stage game-theoretic model to reveal the coupling game among heterogeneous passengers, heterogeneous drivers, and the ride-sourcing platform in the on-demand ride services market regulated by the government. In this model, passengers with heterogeneous willingness to pay have heterogeneous coupon costs, while drivers are divided into part-time drivers and full-time drivers. We analyze the endogenous influence of platform subsidy strategies on drivers' and passengers' decision-making during surge and regular periods. Numerical experiments are conducted to illustrate the response curves to platform subsidies. The analytical and numerical results show that when the subsidy on passengers is moderate, the rapid demand growth during the regular period can be achieved. Precise subsidies on part-time drivers can achieve the same effect as subsidizing both part-time and full-time drivers, which increases supply and generates higher profits at a lower subsidy cost. Platform subsidies benefit the platform, passengers, and drivers, but may increase driving fatigue. When the subsidy budget is limited, the platform should prioritize subsidizing passengers before providing subsidies for drivers during the surge period. Finally, we discuss the impacts of governmental regulation on different stakeholders. This research gains insights into optimizing subsidy strategies of the ride-sourcing platform under government regulation and budget constraint, and the results provide suggestions for the platform and support government decision-making.