research-article

Spatio-temporal pricing for ridesharing platforms

Authors:

David C. ParkesAuthors Info & Claims

ACM SIGecom Exchanges, Volume 18, Issue 2

Pages 53 - 57

https://doi.org/10.1145/3440968.3440975

Published: 02 December 2020 Publication History

Abstract

Ridesharing platforms match drivers and riders to trips, using dynamic prices to balance supply and demand. A challenge is to set prices that are appropriately smooth in space and time, so that drivers will choose to accept their dispatched trips, rather than drive to another area or wait for higher prices or a better trip. We work in a complete information, discrete time, multi-period, multi-location model, and introduce the Spatio-Temporal Pricing (STP) mechanism. The mechanism is incentive-aligned, in that it is a subgame-perfect equilibrium for drivers to always accept their trip dispatches. The mechanism is also welfare-optimal, envy-free, individually rational, budget balanced and core-selecting in equilibrium from any history onward. The proof of incentive alignment makes use of the M^♯ concavity of minimum cost flow objectives. We also give an impossibility result, that there can be no dominant-strategy mechanism with the same economic properties. Simulation results suggest that the STP mechanism can achieve significantly higher social welfare than a myopic pricing mechanism.

References

[1]

Campbell, H. 2016. Advice for new uber drivers - don't chase the surge! https://maximumridesharingprofits.com/advice-new-uber-drivers-dont-chase-surge/. [Online; accessed 8-January-2018].

[2]

Chaudhari, H. A., Byers, J. W., and Terzi, E. 2018. Putting data in the driver's seat: Optimizing earnings for on-demand ride-hailing. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. ACM, 90--98.

Digital Library

[3]

Chen, K. 2017. Dynamic pricing in a labor market: Surge pricing & flexible work on uber. https://www.youtube.com/watch?v=qI094xqMiPA. [Online; accessed 8-January-2018].

[4]

Chen, M. K., Rossi, P. E., Chevalier, J. A., and Oehlsen, E. 2019. The value of flexible work: Evidence from uber drivers. Journal of Political Economy 127, 6, 2735--2794.

[5]

Cook, C., Diamond, R., Hall, J., List, J., Oyer, P., et al. 2018. The gender earnings gap in the gig economy: Evidence from over a million rideshare drivers. Tech. rep., Stanford University.

[6]

Foroohar, R. 2015. Time person of the year 2015 runner-up: Travis kalanick. https://time.com/time-person-of-the-year-2015-runner-up-travis-kalanick/. [Online; accessed 1-September-2017].

[7]

Gridwise. 2017. This is how rideshare drivers are taking advantage of football season. https://gridwise.io/this-is-how-rideshare-drivers-are-taking-advantage-of-football-season. [Online; accessed 1-September-2018].

[8]

Hall, J. V. and Krueger, A. B. 2016. An analysis of the labor market for uber's driver-partners in the united states. NBER Working Paper No. 22843.

[9]

Lyft. 2016. 6 reasons to drive with lyft. https://blog.lyft.com/posts/reasons-to-drive-with-lyft. [Online; accessed 12-December-2017].

[10]

Lyft. 2017. Pricing when it's busy. https://help.lyft.com/hc/en-us/articles/115012926227. [Online; accessed 1-September-2017].

[11]

Marshall, A. 2020. Uber changes its rules, and drivers adjust their strategies. https://www.wired.com/story/uber-changes-rules-drivers-adjust-strategies/amp. [Online; accessed 17-August-2020].

[12]

Murota, K. 2003. Discrete convex analysis. SIAM.

[13]

Paul, K. 2018. There's a quiet battle of wills between uber drivers and customers over who cancels first. https://www.marketwatch.com/story/new-york-citys-new-uber-rules-could-make-those-5-cancellation-fees-go-away-2018-08-16. [Online; accessed 17-September-2018].

[14]

Rayle, L., Shaheen, S., Chan, N., Dai, D., and Cervero, R. 2014. App-based, on-demand ride services: Comparing taxi and ridesourcing trips and user characteristics in san francisco. Tech. rep., University of California Transportation Center (UCTC).

[15]

Uber. 2017. Drive with uber - make money on your schedule. https://www.uber.com/us/en/drive/. [Online; accessed 12-December-2017].

[16]

Uber. 2019. To california drivers: keeping you in the driver's seat. https://www.uber.com/blog/california/keeping-you-in-the-drivers-seat-1/. [Online; accessed 17-August-2020].

Cited By

Zheng YHao QWang JGao CChen JJin DLi Y(2024)A Survey of Machine Learning for Urban Decision Making: Applications in Planning, Transportation, and HealthcareACM Computing Surveys10.1145/369598657:4(1-41)Online publication date: 22-Nov-2024
https://dl.acm.org/doi/10.1145/3695986
Wang JFeng SYang H(2024)Spatial–Temporal Upfront Pricing Under a Mixed Pooling and Non-Pooling Market With Reinforcement LearningIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.341450425:11(17628-17649)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3414504
Ao DGao JLai ZLi S(2024)Regulating transportation network companies with a mixture of autonomous vehicles and for-hire human driversTransportation Research Part A: Policy and Practice10.1016/j.tra.2024.103975180(103975)Online publication date: Feb-2024
https://doi.org/10.1016/j.tra.2024.103975
Show More Cited By

Recommendations

Dynamic pricing in ridesharing platforms

We study dynamic pricing policies for ridesharing platforms such as Lyft and Uber. On one hand these platforms are two-sided: this requires economic models that capture the incentives of both drivers and passengers. On the other hand, these platforms ...
Spatio-Temporal Pricing for Ridesharing Platforms
Ridesharing platforms have radically changed the way people get around in urban areas, but there remain challenges undercutting the mission of “making transportation as reliable as running water.” A particular concern is that drivers strategize: calling ...
Ridesharing platforms match drivers and riders to trips, using dynamic prices to balance supply and demand. A challenge is to set prices that are appropriately smooth in space and time, so that drivers with the flexibility to decide how to work will ...
Spatio-Temporal Pricing for Ridesharing Platforms
EC '19: Proceedings of the 2019 ACM Conference on Economics and Computation

Ridesharing platforms match drivers and riders to trips, using dynamic prices to balance supply and demand. Despite having radically changed the way people get around in urban areas, there still remain a number of major challenges, undercutting their ...

Comments

Information & Contributors

Information

Published In

cover image ACM SIGecom Exchanges

ACM SIGecom Exchanges Volume 18, Issue 2

November 2020

59 pages

EISSN:1551-9031

DOI:10.1145/3440968

Issue’s Table of Contents

Copyright © 2020 Copyright is held by the owner/author(s).

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 December 2020

Published in SIGECOM Volume 18, Issue 2

Check for updates

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

16
Total Citations
View Citations
354
Total Downloads

Downloads (Last 12 months)80
Downloads (Last 6 weeks)13

Reflects downloads up to 13 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zheng YHao QWang JGao CChen JJin DLi Y(2024)A Survey of Machine Learning for Urban Decision Making: Applications in Planning, Transportation, and HealthcareACM Computing Surveys10.1145/369598657:4(1-41)Online publication date: 22-Nov-2024
https://dl.acm.org/doi/10.1145/3695986
Wang JFeng SYang H(2024)Spatial–Temporal Upfront Pricing Under a Mixed Pooling and Non-Pooling Market With Reinforcement LearningIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.341450425:11(17628-17649)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3414504
Ao DGao JLai ZLi S(2024)Regulating transportation network companies with a mixture of autonomous vehicles and for-hire human driversTransportation Research Part A: Policy and Practice10.1016/j.tra.2024.103975180(103975)Online publication date: Feb-2024
https://doi.org/10.1016/j.tra.2024.103975
Aleksandrov M(2023)Manipulating Waiting-Plus-Detour-Time Mechanisms for Pickup and Delivery ProblemsOperations Research Proceedings 202210.1007/978-3-031-24907-5_53(443-449)Online publication date: 30-Aug-2023
https://doi.org/10.1007/978-3-031-24907-5_53
Benjaafar SJiang DLi XLi X(2022)Dynamic Inventory Repositioning in On-Demand Rental NetworksManagement Science10.1287/mnsc.2021.428668:11(7861-7878)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1287/mnsc.2021.4286
Shao SMittal ATwumasi-Boakye RGupta A(2022)Fair Pricing of Ridehailing Services With Asymmetric Demand and Travel TimeIEEE Transactions on Control of Network Systems10.1109/TCNS.2021.31242649:2(670-681)Online publication date: Jun-2022
https://doi.org/10.1109/TCNS.2021.3124264
Zhu JXie NCai ZTang WChen X(2022)A comprehensive review of shared mobility for sustainable transportation systemsInternational Journal of Sustainable Transportation10.1080/15568318.2022.205439017:5(527-551)Online publication date: 28-Mar-2022
https://doi.org/10.1080/15568318.2022.2054390
Qin ZZhu HYe J(2022)Reinforcement learning for ridesharing: An extended surveyTransportation Research Part C: Emerging Technologies10.1016/j.trc.2022.103852144(103852)Online publication date: Nov-2022
https://doi.org/10.1016/j.trc.2022.103852
Tafreshian AMasoud N(2022)A truthful subsidy scheme for a peer-to-peer ridesharing market with incomplete informationTransportation Research Part B: Methodological10.1016/j.trb.2022.05.010162(130-161)Online publication date: Aug-2022
https://doi.org/10.1016/j.trb.2022.05.010
Qin ZZhu HYe J(2021)Reinforcement Learning for Ridesharing: A Survey2021 IEEE International Intelligent Transportation Systems Conference (ITSC)10.1109/ITSC48978.2021.9564924(2447-2454)Online publication date: 19-Sep-2021
https://dl.acm.org/doi/10.1109/ITSC48978.2021.9564924
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents