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Spatial pricing in ride-sourcing markets under a congestion charge

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  • Li, Sen
  • Yang, Hai
  • Poolla, Kameshwar
  • Varaiya, Pravin

Abstract

This paper studies the optimal spatial pricing for a ride-sourcing platform subject to a congestion charge. The platform determines the ride prices over the transportation network to maximize its profit, while the regulatory agency imposes the congestion charge to reduce traffic congestion in the urban core. A network economic equilibrium model is proposed to capture the intimate interactions among passenger demand, driver supply, passenger and driver waiting times, platform pricing, vehicle repositioning and flow balance over the transportation network. The overall optimal pricing problem is cast as a non-convex program. An algorithm is proposed to approximately compute its optimal solution, and a tight upper bound is established to evaluate its performance loss with respect to the globally optimal solution. Using the proposed model, we compare the impacts of three forms of congestion charge: (a) a one-directional cordon charge on ride-sourcing vehicles that enter the congestion area; (b) a bi-directional cordon charge on ride-sourcing vehicles that enter or exit the congestion area; (c) a trip-based congestion charge on all ride-sourcing trips. We show that the one-directional congestion charge not only reduces the ride-sourcing traffic in the congestion area, but also reduces the travel cost outside the congestion zone and benefits passengers in these underserved areas. We establish that in all congestion charge schemes the largest share of the tax burden is carried by the platforms, as opposed to passengers and drivers. We further show that compared to other congestion charges, the one-directional cordon charge is more effective in congestion mitigation: to achieve the same congestion-mitigation target, it imposes a smaller cost on passengers, drivers, and the platform. On the other hand, compared with the other charges, the trip-based congestion charge is more effective in revenue-raising: to raise the same tax revenue, it leads to a smaller loss to passengers, drivers, and the platform. We validate these results through realistic numerical studies for San Francisco.

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  • Li, Sen & Yang, Hai & Poolla, Kameshwar & Varaiya, Pravin, 2021. "Spatial pricing in ride-sourcing markets under a congestion charge," Transportation Research Part B: Methodological, Elsevier, vol. 152(C), pages 18-45.
    • Handle: RePEc:eee:transb:v:152:y:2021:i:c:p:18-45
    DOI: 10.1016/j.trb.2021.07.004
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    Cited by:

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    6. Jing Gao & Sen Li, 2023. "Regulating For-Hire Autonomous Vehicles for An Equitable Multimodal Transportation Network," Papers 2301.05798, arXiv.org, revised Oct 2023.
    7. Qingyang Xiao & Jee Eun Kang, 2023. "Pricing in emerging mobility services: a comprehensive review," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(6), pages 482-500, December.
    8. Liang, Yuan & Yu, Bingjie & Zhang, Xiaojian & Lu, Yi & Yang, Linchuan, 2023. "The short-term impact of congestion taxes on ridesourcing demand and traffic congestion: Evidence from Chicago," Transportation Research Part A: Policy and Practice, Elsevier, vol. 172(C).
    9. Liu, Yining & Ouyang, Yanfeng, 2023. "Planning ride-pooling services with detour restrictions for spatially heterogeneous demand: A multi-zone queuing network approach," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    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. Gao, Jing & Li, Sen, 2024. "Regulating for-hire autonomous vehicles for an equitable multimodal transportation network," Transportation Research Part B: Methodological, Elsevier, vol. 183(C).
    12. 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.
    13. Ao, Di & Lai, Zhijie & Li, Sen, 2024. "Control of dynamic ride-hailing networks with a mixed fleet of autonomous vehicles and for-hire human drivers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 189(C).
    14. 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).
    15. Yuan Liang & Bingjie Yu & Xiaojian Zhang & Yi Lu & Linchuan Yang, 2022. "The Short-term Impact of Congestion Taxes on Ridesourcing Demand and Traffic Congestion: Evidence from Chicago," Papers 2207.01793, arXiv.org, revised Feb 2023.
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