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Optimizing the economic and environmental benefits of ride‐hailing and pooling

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  • Sergey Naumov
  • David Keith

Abstract

Ride‐hailing platforms such as Uber and Lyft promise to reduce the negative externalities of driving and improve access to transportation. However, recent empirical evidence has been mixed about the impact of ride‐hailing on US cities, often resulting in a net increase in traffic congestion and greenhouse gas (GHG) emissions, largely due to increased travel demand and competition with public transit. Pooled rides, in which multiple passengers share a single vehicle, are an effective solution to improve the sustainability of ride‐hailing, reducing GHG emissions and traffic congestion and appealing to price‐sensitive population segments by offering relatively cheaper rides. Yet, most ride‐hailing trips are unprofitable currently, resulting from ride‐hailing rides being subsidized (especially pooled) to compete with cheaper transportation alternatives such as public transit. In this paper, we consider whether price optimization can be used to improve ride‐hailing revenues while also reducing the environmental impacts of ride‐hailing, particularly as the cost of ride‐hailing is expected to fall into the future with the introduction of automated vehicles. Using a discrete choice experiment and multinomial logit choice model with a representative sample of the US population, we estimate consumer preferences for the attributes of ride‐hailing services and use them to explore how ride prices affect the revenue of ride‐hailing platforms and the total vehicle miles traveled (VMT) by the ride‐hailing fleet. We show that as the costs of driving fall, continuously increasing the difference between the prices of individual and pooled rides is financially optimal for ride‐hailing platforms. Importantly, this pricing strategy also significantly reduces total VMT, resulting in a win–win for ride‐hailing platforms and cities. We perform extensive sensitivity analyses and show that our results are qualitatively robust under a wide range of consumer preferences and market conditions but that the optimal trajectory of prices and realized gains vary, highlighting opportunities for ride‐hailing services to influence the future of urban transportation.

Suggested Citation

  • 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.
  • Handle: RePEc:bla:popmgt:v:32:y:2023:i:3:p:904-929
    DOI: 10.1111/poms.13905
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    as
    1. 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.
    2. Ioannis Bellos & Mark Ferguson & L. Beril Toktay, 2017. "The Car Sharing Economy: Interaction of Business Model Choice and Product Line Design," Manufacturing & Service Operations Management, INFORMS, vol. 19(2), pages 185-201, May.
    3. Yap, Menno D. & Correia, Gonçalo & van Arem, Bart, 2016. "Preferences of travellers for using automated vehicles as last mile public transport of multimodal train trips," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 1-16.
    4. Maxime C. Cohen, 2018. "Big Data and Service Operations," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1709-1723, September.
    5. Harish Guda & Upender Subramanian, 2019. "Your Uber Is Arriving: Managing On-Demand Workers Through Surge Pricing, Forecast Communication, and Worker Incentives," Management Science, INFORMS, vol. 67(5), pages 1995-2014, May.
    6. 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.
    7. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, January.
    8. Hongmin Li & Woonghee Tim Huh, 2011. "Pricing Multiple Products with the Multinomial Logit and Nested Logit Models: Concavity and Implications," Manufacturing & Service Operations Management, INFORMS, vol. 13(4), pages 549-563, October.
    9. Amiya K. Chakravarty, 2021. "Blending Capacity on a Rideshare Platform: Independent and Dedicated Drivers," Production and Operations Management, Production and Operations Management Society, vol. 30(8), pages 2522-2546, August.
    10. Zhong, Yuanguang & Yang, Tong & Cao, Bin & Cheng, T.C.E., 2022. "On-demand ride-hailing platforms in competition with the taxi industry: Pricing strategies and government supervision," International Journal of Production Economics, Elsevier, vol. 243(C).
    11. Brownstone, David & Bunch, David S. & Train, Kenneth, 2000. "Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 34(5), pages 315-338, June.
    12. Zhong, Yuanguang & Lin, Zhaozhan & Zhou, Yong-Wu & Cheng, T.C.E. & Lin, Xiaogang, 2019. "Matching supply and demand on ride-sharing platforms with permanent agents and competition," International Journal of Production Economics, Elsevier, vol. 218(C), pages 363-374.
    13. K. J. Arrow, 1971. "The Economic Implications of Learning by Doing," Palgrave Macmillan Books, in: F. H. Hahn (ed.), Readings in the Theory of Growth, chapter 11, pages 131-149, Palgrave Macmillan.
    14. Chen, T. Donna & Kockelman, Kara M. & Hanna, Josiah P., 2016. "Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle & charging infrastructure decisions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 243-254.
    15. Nikhil Garg & Hamid Nazerzadeh, 2019. "Driver Surge Pricing," Papers 1905.07544, arXiv.org, revised Mar 2021.
    16. Sergey Naumov & David R. Keith & Charles H. Fine, 2020. "Unintended Consequences of Automated Vehicles and Pooling for Urban Transportation Systems," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1354-1371, May.
    17. Ma, Jie & Xu, Min & Meng, Qiang & Cheng, Lin, 2020. "Ridesharing user equilibrium problem under OD-based surge pricing strategy," Transportation Research Part B: Methodological, Elsevier, vol. 134(C), pages 1-24.
    18. Argote, L. & Epple, D., 1990. "Learning Curves In Manufacturing," GSIA Working Papers 89-90-02, Carnegie Mellon University, Tepper School of Business.
    19. Wei Qi & Lefei Li & Sheng Liu & Zuo-Jun Max Shen, 2018. "Shared Mobility for Last-Mile Delivery: Design, Operational Prescriptions, and Environmental Impact," Manufacturing & Service Operations Management, INFORMS, vol. 20(4), pages 737-751, October.
    20. Mi Diao & Hui Kong & Jinhua Zhao, 2021. "Impacts of transportation network companies on urban mobility," Nature Sustainability, Nature, vol. 4(6), pages 494-500, June.
    21. Hwang, Keith & Giuliano, Genevieve, 1990. "The Determinants of Ridesharing: Literature Review," University of California Transportation Center, Working Papers qt3r91r3r4, University of California Transportation Center.
    22. John D. Sterman & Rebecca Henderson & Eric D. Beinhocker & Lee I. Newman, 2007. "Getting Big Too Fast: Strategic Dynamics with Increasing Returns and Bounded Rationality," Management Science, INFORMS, vol. 53(4), pages 683-696, April.
    23. Joseph K. Goodman & Gabriele Paolacci, 2017. "Crowdsourcing Consumer Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(1), pages 196-210.
    24. Tingting Wang & Cynthia Chen, 2014. "Impact of fuel price on vehicle miles traveled (VMT): do the poor respond in the same way as the rich?," Transportation, Springer, vol. 41(1), pages 91-105, January.
    25. Linda Argote & Sara L. Beckman & Dennis Epple, 1990. "The Persistence and Transfer of Learning in Industrial Settings," Management Science, INFORMS, vol. 36(2), pages 140-154, February.
    26. Travis Tokar & John Aloysius & Matthew Waller & Doyle L. Hawkins, 2016. "Exploring Framing Effects in Inventory Control Decisions: Violations of Procedure Invariance," Production and Operations Management, Production and Operations Management Society, vol. 25(2), pages 306-329, February.
    27. Susan Shaheen & Adam Cohen, 2019. "Shared ride services in North America: definitions, impacts, and the future of pooling," Transport Reviews, Taylor & Francis Journals, vol. 39(4), pages 427-442, July.
    28. Hwang, Keith & Giuliano, Genevieve, 1990. "The Determinants of Ridesharing: Literature Review," University of California Transportation Center, Working Papers qt0gd0d2fj, University of California Transportation Center.
    29. David R. Keith & Jeroen J.R. Struben & Sergey Naumov, 2020. "The Diffusion of Alternative Fuel Vehicles: A Generalised Model and Future Research Agenda," Journal of Simulation, Taylor & Francis Journals, vol. 14(4), pages 260-277, October.
    30. Brownstone, David & Bunch, David S. & Train, Kenneth, 2000. "Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 34(5), pages 315-338, June.
    31. Sumantran, Venkat & Fine, Charles & Gonsalvez, David, 2017. "Faster, Smarter, Greener: The Future of the Car and Urban Mobility," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262036665, December.
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