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Dynamic optimization strategies for on-demand ride services platform: Surge pricing, commission rate, and incentives

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  • Chen, Xiqun (Michael)
  • Zheng, Hongyu
  • Ke, Jintao
  • Yang, Hai

Abstract

On-demand ride services reshape urban transportation systems, human mobility, and travelers' mode choice behavior. Compared to the traditional street-hailing taxi, an on-demand ride services platform analyzes ride requests of passengers and coordinates real-time supply and demand with dynamic operational strategies in the ride-sourcing market. To test the impact of dynamic optimization strategies on the ride-sourcing market, this paper proposes a dynamic vacant car-passenger meeting model. In this model, the accumulative arrival rate and departure rate of passengers and vacant cars determine the waiting number of passengers and vacant cars, while the waiting number of passengers and vacant cars in turn influence the meeting rate (which equals to the departure rate of both passengers and vacant cars). The departure rate means the rate at which passengers and vacant cars match up and start a paid trip. Compared with classic equilibrium models, this model can be utilized to characterize the influence of short-term variances and disturbances of current demand and supply (i.e., arrival rates of passengers and vacant cars) on the waiting numbers of passengers and vacant cars. Using the proposed meeting model, we optimize dynamic strategies under two objective functions, i.e., platform revenue maximization, and social welfare maximization, while the driver's profit is guaranteed above a certain level. We also propose an algorithm based on approximate dynamic programming (ADP) to solve the sequential dynamic optimization problem. The results show that our algorithm can effectively improve the objective function of the multi-period problem, compared with the myopic algorithm. A broader range of surge pricing and commission rate and the introduction of incentives are helpful to achieve better optimization results. The dynamic optimization strategies help the on-demand ride services platform efficiently adjust supply and demand resources and achieve specific optimization goals.

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  • Chen, Xiqun (Michael) & Zheng, Hongyu & Ke, Jintao & Yang, Hai, 2020. "Dynamic optimization strategies for on-demand ride services platform: Surge pricing, commission rate, and incentives," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 23-45.
    • Handle: RePEc:eee:transb:v:138:y:2020:i:c:p:23-45
    DOI: 10.1016/j.trb.2020.05.005
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    Cited by:

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    5. Wang, Dujuan & Wang, Qi & Yin, Yunqiang & Cheng, T.C.E., 2023. "Optimization of ride-sharing with passenger transfer via deep reinforcement learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 172(C).
    6. Di Ao & Jing Gao & Zhijie Lai & Sen Li, 2021. "Regulating Transportation Network Companies with a Mixture of Autonomous Vehicles and For-Hire Human Drivers," Papers 2112.07218, arXiv.org, revised Dec 2023.
    7. Mo, Dong & Chen, Xiqun (Michael) & Zhang, Junlin, 2022. "Modeling and Managing Mixed On-Demand Ride Services of Human-Driven Vehicles and Autonomous Vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 80-119.
    8. Ke, Jintao & Chen, Xiqun (Michael) & Yang, Hai & Li, Sen, 2022. "Coordinating supply and demand in ride-sourcing markets with pre-assigned pooling service and traffic congestion externality," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    9. Ke, Jintao & Yang, Hai & Li, Xinwei & Wang, Hai & Ye, Jieping, 2020. "Pricing and equilibrium in on-demand ride-pooling markets," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 411-431.
    10. Sun, Luoyi & Teunter, Ruud H. & Hua, Guowei & Wu, Tian, 2020. "Taxi-hailing platforms: Inform or Assign drivers?," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 197-212.
    11. Jun Tu & Juan Du & Min Huang, 2023. "Competition between Green and Non-Green Travel Companies: The Role of Governmental Subsidies in Green Travel," Sustainability, MDPI, vol. 15(9), pages 1-33, May.
    12. He, Shan & Dai, Ying & Ma, Zu-Jun, 2023. "To offer or not to offer? The optimal value-insured strategy for crowdsourced delivery platforms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    13. Liu, Yang & Li, Sen, 2023. "An economic analysis of on-demand food delivery platforms: Impacts of regulations and integration with ride-sourcing platforms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).
    14. Zhu, Zheng & Xu, Ailing & He, Qiao-Chu & Yang, Hai, 2021. "Competition between the transportation network company and the government with subsidies to public transit riders," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    15. Tubagus Robbi Megantara & Sudradjat Supian & Diah Chaerani, 2022. "Strategies to Reduce Ride-Hailing Fuel Consumption Caused by Pick-Up Trips: A Mathematical Model under Uncertainty," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
    16. Mo, Dong & Yu, Jingru & Chen, Xiqun Michael, 2020. "Modeling and managing heterogeneous ride-sourcing platforms with government subsidies on electric vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 447-472.
    17. Zhao, Meng & Li, Bin & Ren, Jiali & Hao, Zhihua, 2023. "Competition equilibrium of ride-sourcing platforms and optimal government subsidies considering customers’ green preference under peak carbon dioxide emissions," International Journal of Production Economics, Elsevier, vol. 255(C).
    18. Meijian Yang & Enjun Xia, 2021. "A Systematic Literature Review on Pricing Strategies in the Sharing Economy," Sustainability, MDPI, vol. 13(17), pages 1-28, August.

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