poster

Free access

Optimal Vehicle Dispatching for Ride-sharing Platforms via Dynamic Pricing

Authors:

Pingzhong Tang,

Song ZuoAuthors Info & Claims

WWW '18: Companion Proceedings of the The Web Conference 2018

Pages 51 - 52

https://doi.org/10.1145/3184558.3186924

Published: 23 April 2018 Publication History

All formats PDF

Abstract

Over the past few years, ride-sharing has been proven to be an effective way to relieve urban traffic congestion, as evidenced by several emerging ride-sharing platforms such as Uber and Didi. A key economic problem for these platforms is to design a revenue-optimal (or welfare-optimal) pricing scheme and a corresponding vehicle dispatching policy that incorporates geographic information, and more importantly, dynamic supply and demand. In this paper, we aim to solve this problem by introducing a unified model that takes into account both travel time and driver redirection. We tackle the non-convexity problem using the "ironing" technique and formulate the optimization problem as a Markov decision process (MDP), where the states are the driver distributions and the decision variables are the prices. Our main finding is to give an efficient algorithm that computes the exact revenue (or welfare) optimal randomized pricing schemes. We characterize the optimal solutions of the MDP by primal-dual analysis of a convex program. We also conduct empirical analysis of our solution with real data of a major ride-sharing platform and show its significant advantages over fixed pricing schemes as well as those prevalent surge-based pricing schemes.

References

[1]

Santiago Balseiro, Max Lin, Vahab Mirrokni, Renato Paes Leme, and Song Zuo. 2017. Dynamic revenue sharing. In NIPS 2017.

[2]

Siddhartha Banerjee, Carlos Riquelme, and Ramesh Johari. 2015. Pricing in Ride-share Platforms: A Queueing-Theoretic Approach. (2015).

Digital Library

[3]

Gerard P Cachon, Kaitlin M Daniels, and Ruben Lobel. 2016. The role of surge pricing on a service platform with self-scheduling capacity. (2016).

[4]

Juan Camilo Castillo, Dan Knoepfle, and Glen Weyl. 2017. Surge pricing solves the wild goose chase. In EC' 17. ACM, 241--242.

Digital Library

[5]

Mengjing Chen, Weiran Shen, Pingzhong Tang, and Song Zuo. 2017. Optimal Vehicle Dispatching Schemes via Dynamic Pricing. arXiv:1707.01625 (2017).

[6]

M Keith Chen and Michael Sheldon. 2015. Dynamic pricing in a labor market: Surge pricing and flexible work on the Uber platform. Technical Report.

[7]

Judd Cramer and Alan B Krueger. 2016. Disruptive change in the taxi business: The case of Uber. The American Economic Review Vol. 106, 5 (2016), 177--182.

[8]

Vincent P Crawford and Juanjuan Meng. 2011. New york city cab drivers' labor supply revisited: Reference-dependent preferences with rationalexpectations targets for hours and income. The American Economic Review Vol. 101, 5 (2011).

[9]

Zhixuan Fang, Longbo Huang, and Adam Wierman. 2017. Prices and subsidies in the sharing economy. In WWW 2017. 53--62.

Digital Library

[10]

Michel Gendreau, Alain Hertz, and Gilbert Laporte. 1994. A tabu search heuristic for the vehicle routing problem. Management science Vol. 40, 10 (1994), 1276--1290.

[11]

Gianpaolo Ghiani, Francesca Guerriero, Gilbert Laporte, and Roberto Musmanno. 2003. Real-time vehicle routing: Solution concepts, algorithms and parallel computing strategies. European Journal of Operational Research Vol. 151, 1 (2003).

[12]

Peter F Kostiuk. 1990. Compensating differentials for shift work. Journal of political Economy Vol. 98, 5, Part 1 (1990), 1054--1075.

[13]

Gilbert Laporte. 1992. The vehicle routing problem: An overview of exact and approximate algorithms. European journal of operational research Vol. 59, 3 (1992).

[14]

R Preston McAfee and Vera Te Velde. 2006. Dynamic pricing in the airline industry. forthcoming in Handbook on Economics and Information Systems, Ed: TJ Hendershott, Elsevier (2006).

[15]

Gerald S Oettinger. 1999. An empirical analysis of the daily labor supply of stadium venors. Journal of political Economy Vol. 107, 2 (1999), 360--392.

[16]

Joanna Stavins. 2001. Price discrimination in the airline market: The effect of market concentration. Review of Economics and Statistics Vol. 83, 1 (2001), 200--202.

Cited By

Zhou ZChen RDeng W(2024)A Delaunay triangulation based dynamic pricing approach to profit‐driven crowdsourced package deliveryIET Intelligent Transport Systems10.1049/itr2.1243618:6(984-1003)Online publication date: 19-Mar-2024
https://doi.org/10.1049/itr2.12436
Xiao QKang J(2023)Pricing in emerging mobility services: a comprehensive reviewJournal of Revenue and Pricing Management10.1057/s41272-023-00423-z22:6(482-500)Online publication date: 4-Mar-2023
https://doi.org/10.1057/s41272-023-00423-z
Sun LHou YLi Z(2023)Explicit Assignment and Dynamic Pricing of Macro Online Tasks in Spatial CrowdsourcingDatabase Systems for Advanced Applications10.1007/978-3-031-30637-2_30(464-480)Online publication date: 17-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-30637-2_30
Show More Cited By

Index Terms

Optimal Vehicle Dispatching for Ride-sharing Platforms via Dynamic Pricing
1. Theory of computation
  1. Theory and algorithms for application domains
    1. Algorithmic game theory and mechanism design
      1. Computational pricing and auctions

Recommendations

Pricing in Ride-Sharing Platforms: A Queueing-Theoretic Approach
EC '15: Proceedings of the Sixteenth ACM Conference on Economics and Computation

We study optimal pricing strategies for ride-sharing platforms, using a queueing-theoretic economic model. Analysis of pricing in such settings is complex: On one hand these platforms are two-sided - this requires economic models that capture the ...
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 ...
An On-line Truthful and Individually Rational Pricing Mechanism for Ride-sharing
SIGSPATIAL '17: Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Ride-sharing has the potential of addressing many socioeconomic challenges related to transportation. The rising popularity of ride-sharing platforms (e.g., Uber, Lyft, DiDi) in addition to the emergence of new applications like food delivery and ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '18: Companion Proceedings of the The Web Conference 2018

April 2018

2023 pages

ISBN:9781450356404

General Chairs:
Pierre-Antoine Champin
Université Claude Bernard Lyon 1, France
,
Fabien Gandon
Inria, Université Côte d'Azur, CNRS, I3S, France
,
Lionel Médini
Université Claude Bernard Lyon 1, CNRS, LIRIS, France
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Panagiotis G. Ipeirotis
New York University, USA

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

IW3C2: International World Wide Web Conference Committee

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 23 April 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Funding Sources

China Youth 1000-talent program
the National Natural Science Foundation of China Grant
Alibaba Innovative Research program

Conference

WWW '18

Sponsor:

IW3C2

WWW '18: The Web Conference 2018

April 23 - 27, 2018

Lyon, France

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
1,173
Total Downloads

Downloads (Last 12 months)244
Downloads (Last 6 weeks)30

Reflects downloads up to 10 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhou ZChen RDeng W(2024)A Delaunay triangulation based dynamic pricing approach to profit‐driven crowdsourced package deliveryIET Intelligent Transport Systems10.1049/itr2.1243618:6(984-1003)Online publication date: 19-Mar-2024
https://doi.org/10.1049/itr2.12436
Xiao QKang J(2023)Pricing in emerging mobility services: a comprehensive reviewJournal of Revenue and Pricing Management10.1057/s41272-023-00423-z22:6(482-500)Online publication date: 4-Mar-2023
https://doi.org/10.1057/s41272-023-00423-z
Sun LHou YLi Z(2023)Explicit Assignment and Dynamic Pricing of Macro Online Tasks in Spatial CrowdsourcingDatabase Systems for Advanced Applications10.1007/978-3-031-30637-2_30(464-480)Online publication date: 17-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-30637-2_30
Yang YCheng YYuan YWang GChen LSun Y(2022)Privacy-preserving cooperative online matching over spatial crowdsourcing platformsProceedings of the VLDB Endowment10.14778/3561261.356126616:1(51-63)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.14778/3561261.3561266
Massey WEkwedike EHampshire RPender J(2022)A transient symmetry analysis for the M/M/1/k queueQueueing Systems: Theory and Applications10.1007/s11134-022-09849-5103:1-2(1-43)Online publication date: 7-Aug-2022
https://dl.acm.org/doi/10.1007/s11134-022-09849-5
Yang YDing YYuan DWang GXie XLiu YHe TZhang D(2020)TransLocProceedings of the 26th Annual International Conference on Mobile Computing and Networking10.1145/3372224.3419198(1-14)Online publication date: 16-Apr-2020
https://dl.acm.org/doi/10.1145/3372224.3419198
Yu XShen S(2020)An Integrated Decomposition and Approximate Dynamic Programming Approach for On-Demand Ride PoolingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2019.293442321:9(3811-3820)Online publication date: Sep-2020
https://doi.org/10.1109/TITS.2019.2934423
Cheng YLi BZhou XYuan YWang GChen L(2020)Real-Time Cross Online Matching in Spatial Crowdsourcing2020 IEEE 36th International Conference on Data Engineering (ICDE)10.1109/ICDE48307.2020.00008(1-12)Online publication date: Apr-2020
https://doi.org/10.1109/ICDE48307.2020.00008
Wang HYang H(2019)Ridesourcing systems: A framework and reviewTransportation Research Part B: Methodological10.1016/j.trb.2019.07.009129(122-155)Online publication date: Nov-2019
https://doi.org/10.1016/j.trb.2019.07.009
Tong YZhou ZZeng YChen LShahabi C(2019)Spatial crowdsourcing: a surveyThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-019-00568-729:1(217-250)Online publication date: 29-Aug-2019
https://dl.acm.org/doi/10.1007/s00778-019-00568-7

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

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

Figures

Tables

Media

View Table of Conten