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We Are on the Way: Analysis of On-Demand Ride-Hailing Systems

Author

Listed:
  • Guiyun Feng

    (Lee Kong Chian School of Business, Singapore Management University, Singapore 178899)

  • Guangwen Kong

    (Department of Marketing and Supply Chain Management, Fox School of Business, Temple University, Philadelphia, Pennsylvania 19122)

  • Zizhuo Wang

    (Institute for Data and Decision Analytics, The Chinese University of Hong Kong, Shenzhen, Shenzhen 518172, China)

Abstract

Problem definition : Recently, there has been a rapid rise of on-demand ride-hailing platforms, such as Uber and Didi, which allow passengers with smartphones to submit trip requests and match them to drivers based on their locations and drivers’ availability. This increased demand has raised questions about how such a new matching mechanism will affect the efficiency of the transportation system—in particular, whether it will help reduce passengers’ average waiting time compared with traditional street-hailing systems. Academic/practical relevance : The on-demand ride-hailing problem has gained much academic interest recently. The results we find in the ride-hailing system have a significant deviation from classic queueing theory where en route time does not play a role. Methodology : In this paper, we shed light on this question by building a stylized model of a circular road and comparing the average waiting times of passengers under various matching mechanisms. Results : We discover the inefficiency in the on-demand ride-hailing system when the en route time is long, which may result in nonmonotonicity of passengers’ average waiting time as the passenger arrival rate increases. After identifying key trade-offs between different mechanisms, we find that the on-demand matching mechanism could result in lower efficiency than the traditional street-hailing mechanism when the system utilization level is medium and the road length is long. Managerial implications : To overcome the disadvantage of both systems, we further propose adding response caps to the on-demand ride-hailing mechanism and develop a heuristic method to calculate a near-optimal cap. We also examine the impact of passenger abandonments, idle time strategies of taxis, and traffic congestion on the performance of the ride-hailing systems. The results of this research would be instrumental for understanding the trade-offs of the new service paradigm and thus enable policy makers to make more informed decisions when enacting regulations for this emerging service paradigm.

Suggested Citation

  • Guiyun Feng & Guangwen Kong & Zizhuo Wang, 2021. "We Are on the Way: Analysis of On-Demand Ride-Hailing Systems," Manufacturing & Service Operations Management, INFORMS, vol. 23(5), pages 1237-1256, September.
    • Handle: RePEc:inm:ormsom:v:23:y:2021:i:5:p:1237-1256
    DOI: 10.1287/msom.2020.0880
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    References listed on IDEAS

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    Cited by:

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    8. Jiayang Li & Guoyin Zhang & Debing Ni, 2025. "Drivers’ Welfare and Pollutant Emission Induced by Ride-Hailing Platforms’ Pricing Strategies," Sustainability, MDPI, vol. 17(9), pages 1-35, April.
    9. Shachaf Ben-Gal & Michal Tzur, 2025. "Data-Driven Policies for the Online Ride-Hailing Problem with Fairness," Transportation Science, INFORMS, vol. 59(3), pages 647-669, June.
    10. Martin, Layla & Minner, Stefan & Pavone, Marco & Schiffer, Maximilian, 2025. "It’s All in the Mix: Technology choice between driverless and human-driven vehicles in sharing systems," European Journal of Operational Research, Elsevier, vol. 324(3), pages 969-980.
    11. Huang, Xingjun & Bu, Yilan & Liu, Junbei & Meng, Meng & Zhang, Jie & Zhuge, Chengxiang, 2025. "A spatial agent-based approach to simulating the ride-hailing system and its environmental impacts," Transport Policy, Elsevier, vol. 174(C).
    12. Juan Camilo Castillo & Dan Knoepfle & E. Glen Weyl, 2025. "Matching and Pricing in Ride Hailing: Wild Goose Chases and How to Solve Them," Management Science, INFORMS, vol. 71(5), pages 4377-4395, May.
    13. Junxin Shi & Xiangyong Li, 2024. "Order assignment in a ride-hailing platform with heterogeneous participants," Operations Management Research, Springer, vol. 17(1), pages 152-174, March.
    14. Xiaogang Lin & Tao Lu & Xin Wang & Gang Kou, 2025. "Mergers Between On-Demand Service Platforms: The Impact on Consumer Surplus and Labor Welfare," Information Systems Research, INFORMS, vol. 36(3), pages 1612-1630, September.
    15. Bin Hu & Ming Hu & Han Zhu, 2022. "Surge Pricing and Two-Sided Temporal Responses in Ride Hailing," Manufacturing & Service Operations Management, INFORMS, vol. 24(1), pages 91-109, January.
    16. Qin Zhou & Jingqi Wang & Yifan Jiao & Jinzhao Du, 2025. "Balancing Supply with Demand on Ride-Hailing Platforms in Markets with Price Regulations: An Operational Approach," Manufacturing & Service Operations Management, INFORMS, vol. 27(5), pages 1515-1531, September.
    17. Wang, Hui & Li, Yuanyuan & Liu, Yang & Hu, Xiaowei & Wang, Jian, 2025. "Optimizing on-demand ride-hailing services in two-sided coupled markets with impatient riders," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 198(C).
    18. Fan Gao & Jingjing Hao & Zhitao Li & Chunyang Han & Jinjun Tang & Chuyun Zhao, 2026. "Understanding inequality in ride-hailing service: an investigation of matching and pickup time," Transportation, Springer, vol. 53(1), pages 229-256, February.
    19. Tushar Shankar Walunj & Shiksha Singhal & Jayakrishnan Nair & Veeraruna Kavitha, 2026. "On the Interplay Between Pricing, Competition and QoS in Ride-Hailing," Dynamic Games and Applications, Springer, vol. 16(1), pages 12-66, March.
    20. Zhang, Kenan & Alonso-Mora, Javier & Fielbaum, Andres, 2025. "What do walking and e-hailing bring to scale economies in on-demand mobility?," Transportation Research Part B: Methodological, Elsevier, vol. 192(C).
    21. Myungeun Eom & Alejandro Toriello, 2026. "Batching and Greedy Policies: How Good Are They in Dynamic Matching?," Manufacturing & Service Operations Management, INFORMS, vol. 28(2), pages 479-495, March.
    22. Saharsh Agarwal & Deepa Mani & Rahul Telang, 2023. "The Impact of Ride-Hailing Services on Congestion: Evidence from Indian Cities," Manufacturing & Service Operations Management, INFORMS, vol. 25(3), pages 862-883, May.
    23. Jaelynn Oh & Chloe Kim Glaeser & Xuanming Su, 2026. "Food Ordering and Delivery: How Platforms and Restaurants Should Split the Pie," Management Science, INFORMS, vol. 72(3), pages 1748-1768, March.
    24. Ziliang Jin & Yulan Wang & Yun Fong Lim & Kai Pan & Zuo-Jun Max Shen, 2023. "Vehicle Rebalancing in a Shared Micromobility System with Rider Crowdsourcing," Manufacturing & Service Operations Management, INFORMS, vol. 25(4), pages 1394-1415, July.
    25. Zhu, Donghao & Minner, Stefan & Bichler, Martin, 2025. "Pricing policy and queue-length disclosure in on-demand service platforms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 204(C).

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