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Integrated reward scheme and surge pricing in a ridesourcing market

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  • Yang, Hai
  • Shao, Chaoyi
  • Wang, Hai
  • Ye, Jieping

Abstract

Surge pricing is commonly used in on-demand ridesourcing platforms to dynamically balance demand and supply, although it is controversial and has long stimulated debate regarding its pros and cons. In practice, there is usually a reasonable or legitimate range of prices. However, such a constrained surge-pricing strategy may fail to balance demand and supply in certain cases—e.g., even adopting the highest allowed price cannot reduce peak-period demand to a level at which the market clears without some form of non-price rationing. To address this limitation, we propose a novel reward scheme integrated with surge pricing: Passengers pay an additional amount to a reward account on top of the regular surge price during peak hours, then use the balance in their reward account to subsidize trips during off-peak hours. We propose models to describe the number of travel requests and the number of active drivers on the platform, and characterize the market equilibrium under several assumptions. We compare scenarios with and without the reward scheme from three perspectives: passenger utility, driver income, and platform revenue and profit. We find that in some situations, all three stakeholders—i.e., passengers, drivers, and the platform—will be better off under the reward scheme integrated with surge pricing.

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  • Yang, Hai & Shao, Chaoyi & Wang, Hai & Ye, Jieping, 2020. "Integrated reward scheme and surge pricing in a ridesourcing market," Transportation Research Part B: Methodological, Elsevier, vol. 134(C), pages 126-142.
  • Handle: RePEc:eee:transb:v:134:y:2020:i:c:p:126-142
    DOI: 10.1016/j.trb.2020.01.008
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    References listed on IDEAS

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    Cited by:

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    3. Huarng, Kun-Huang & Yu, Tiffany Hui-Kuang, 2020. "The impact of surge pricing on customer retention," Journal of Business Research, Elsevier, vol. 120(C), pages 175-180.
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    5. Ke, Jintao & Li, Xinwei & Yang, Hai & Yin, Yafeng, 2021. "Pareto-efficient solutions and regulations of congested ride-sourcing markets with heterogeneous demand and supply," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    6. 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.
    7. Wang, Jing-Peng & Huang, Hai-Jun, 2022. "Operations on an on-demand ride service system with express and limousine," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 348-373.
    8. Vital, Filipe & Ioannou, Petros, 2022. "Balancing of Truck Parking Demand by a Centralized Incentives/Pricing System," Institute of Transportation Studies, Working Paper Series qt3zv2s5jr, Institute of Transportation Studies, UC Davis.
    9. Zhu, Zheng & Ke, Jintao & Wang, Hai, 2021. "A mean-field Markov decision process model for spatial-temporal subsidies in ride-sourcing markets," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 540-565.
    10. 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.
    11. 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.
    12. Zhang, Kenan & Nie, Yu (Marco), 2021. "To pool or not to pool: Equilibrium, pricing and regulation," Transportation Research Part B: Methodological, Elsevier, vol. 151(C), pages 59-90.
    13. Li, Xiaonan & Li, Xiangyong & Wang, Hai & Shi, Junxin & Aneja, Y.P., 2022. "Supply regulation under the exclusion policy in a ride-sourcing market," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 69-94.
    14. Li, Baicheng & Szeto, W.Y. & Luo, Qin, 2021. "A peak-period taxi scheme design problem: Formulation and policy implications," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 146(C).
    15. 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).
    16. Stokkink, Patrick & Geroliminis, Nikolas, 2021. "Predictive user-based relocation through incentives in one-way car-sharing systems," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 230-249.
    17. Tri Basuki Joewono & Ariel Matthew & Muhamad Rizki, 2021. "Loyalty of Paratransit Users in the Era of Competition with Ride Sourcing," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    18. André de Palma & Patrick Stokkink & Nikolas Geroliminis, 2020. "Influence of Dynamic Congestion on Carpooling Matching," THEMA Working Papers 2020-12, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    19. Di, Yining & Xu, Meng & Zhu, Zheng & Yang, Hai & Chen, Xiqun, 2022. "Analysis of ride-sourcing drivers' working Pattern(s) via spatiotemporal work slices: A case study in Hangzhou," Transport Policy, Elsevier, vol. 125(C), pages 336-351.
    20. Dong, Tingting & Xu, Zhengtian & Luo, Qi & Yin, Yafeng & Wang, Jian & Ye, Jieping, 2021. "Optimal contract design for ride-sourcing services under dual sourcing," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 289-313.
    21. Son, Dong-Hoon, 2023. "On-demand ride-sourcing markets with cryptocurrency-based fare-reward scheme," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).
    22. de Palma, André & Stokkink, Patrick & Geroliminis, Nikolas, 2022. "Influence of dynamic congestion with scheduling preferences on carpooling matching with heterogeneous users," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 479-498.
    23. Zhou, Yaqian & Yang, Hai & Ke, Jintao & Wang, Hai & Li, Xinwei, 2022. "Competition and third-party platform-integration in ride-sourcing markets," Transportation Research Part B: Methodological, Elsevier, vol. 159(C), pages 76-103.
    24. Sergey Naumov & David Keith, 2023. "Optimizing the economic and environmental benefits of ride‐hailing and pooling," Production and Operations Management, Production and Operations Management Society, vol. 32(3), pages 904-929, March.
    25. Guo, Xiaotong & Caros, Nicholas S. & Zhao, Jinhua, 2021. "Robust matching-integrated vehicle rebalancing in ride-hailing system with uncertain demand," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 161-189.

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