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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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    Citations

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    Cited by:

    1. Ji, Wen & Liu, Shenglin & Han, Ke & Li, Yanfeng & Liu, Tao, 2024. "The Share-a-Ride Problem with mixed ride-hailing and logistic vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 192(C).
    2. 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.
    3. 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.
    4. 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).

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