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Dynamic pricing and matching in ride‐hailing platforms

Author

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  • Chiwei Yan
  • Helin Zhu
  • Nikita Korolko
  • Dawn Woodard

Abstract

Ride‐hailing platforms such as Uber, Lyft, and DiDi have achieved explosive growth and reshaped urban transportation. The theory and technologies behind these platforms have become one of the most active research topics in the fields of economics, operations research, computer science, and transportation engineering. In particular, advanced matching and dynamic pricing (DP) algorithms—the two key levers in ride‐hailing—have received tremendous attention from the research community and are continuously being designed and implemented at industrial scales by ride‐hailing platforms. We provide a review of matching and DP techniques in ride‐hailing, and show that they are critical for providing an experience with low waiting time for both riders and drivers. Then we link the two levers together by studying a pool‐matching mechanism called dynamic waiting (DW) that varies rider waiting and walking before dispatch, which is inspired by a recent carpooling product Express Pool from Uber. We show using data from Uber that by jointly optimizing DP and DW, price variability can be mitigated, while increasing capacity utilization, trip throughput, and welfare. We also highlight several key practical challenges and directions of future research from a practitioner's perspective.

Suggested Citation

  • Chiwei Yan & Helin Zhu & Nikita Korolko & Dawn Woodard, 2020. "Dynamic pricing and matching in ride‐hailing platforms," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(8), pages 705-724, December.
    • Handle: RePEc:wly:navres:v:67:y:2020:i:8:p:705-724
    DOI: 10.1002/nav.21872
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    Cited by:

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    5. Li, Manzi & Jiang, Gege & Lo, Hong K., 2022. "Pricing strategy of ride-sourcing services under travel time variability," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).
    6. Lei, Zengxiang & Ukkusuri, Satish V., 2023. "Scalable reinforcement learning approaches for dynamic pricing in ride-hailing systems," Transportation Research Part B: Methodological, Elsevier, vol. 178(C).
    7. Bahrami, Sina & Nourinejad, Mehdi & Yin, Yafeng & Wang, Hai, 2023. "The three-sided market of on-demand delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    8. Zhan, Xingbin & Szeto, W.Y. & (Michael) Chen, Xiqun, 2022. "A simulation–optimization framework for a dynamic electric ride-hailing sharing problem with a novel charging strategy," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).
    9. Shi, Junxin & Li, Xiangyong & Aneja, Y.P. & Li, Xiaonan, 2023. "Ride-matching for the ride-hailing platform with heterogeneous drivers," Transport Policy, Elsevier, vol. 136(C), pages 169-192.
    10. Mioara Chirita & George Chirita, 2024. "A Comprehensive Overview of Deep Learning for Algorithmic Pricing in Ride-Sharing Platforms," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 1, pages 177-181.
    11. Soppert, Matthias & Steinhardt, Claudius & Müller, Christian & Gönsch, Jochen & Bhogale, Prasanna M., 2023. "Matching functions for free-floating shared mobility system optimization to capture maximum walking distances," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1194-1214.
    12. Alex Chin & Zhiwei Qin, 2023. "A Unified Representation Framework for Rideshare Marketplace Equilibrium and Efficiency," Papers 2302.14358, arXiv.org.
    13. 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).
    14. 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).
    15. Fleckenstein, David & Klein, Robert & Steinhardt, Claudius, 2023. "Recent advances in integrating demand management and vehicle routing: A methodological review," European Journal of Operational Research, Elsevier, vol. 306(2), pages 499-518.
    16. Zhou, Xizhen & Lv, Mengqi & Ji, Yanjie & Zhang, Shuichao & Liu, Yong, 2023. "Pricing curb parking: Differentiated parking fees or cash rewards?," Transport Policy, Elsevier, vol. 142(C), pages 46-58.
    17. Shetty, Akhil & Li, Sen & Tavafoghi, Hamidreza & Qin, Junjie & Poolla, Kameshwar & Varaiya, Pravin, 2022. "An analysis of labor regulations for transportation network companies," Economics of Transportation, Elsevier, vol. 32(C).
    18. 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.
    19. Zhan, Xingbin & Szeto, W.Y. & (Michael) Chen, Xiqun, 2022. "The dynamic ride-hailing sharing problem with multiple vehicle types and user classes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).

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