research-article

Public Access

ADAPT-pricing: a dynamic and predictive technique for pricing to maximize revenue in ridesharing platforms

Authors:

Mohammad Asghari,

Cyrus ShahabiAuthors Info & Claims

SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 189 - 198

https://doi.org/10.1145/3274895.3274928

Published: 06 November 2018 Publication History

Abstract

Ridesharing platforms use dynamic pricing as a means to control the network's supply and demand at different locations and times (e.g., Lyft's Prime Time and Uber's Surge Pricing) to increase revenue. These algorithms only consider the network's current supply and demand only at a ride's origin to adjust the price of the ride. In this work, we show how we can increase the platform's revenue while lowering the prices as compared to state-of-the-art algorithms, by considering the network's future demand. Furthermore, we show if rather than setting the price of a ride only based on the supply and demand at its origin, we use predictive supply and demand at both the ride's origin and destination, we can further increase the platform's overall revenue. Using a real-world data set from New York City, we show our pricing method can increase the revenue by up to 15% while reducing the price of the rides by an average of 5%. Furthermore, we show that our methods are resilient to up to 25% error in future demand prediction.

References

[1]

Mohammad Asghari and Cyrus Shahabi. An on-line truthful and individually rational pricing mechanism for ride-sharing. In Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL'17, pages 7:1--7:10, 2017.

Digital Library

[2]

Mohammad Asghari, Dingxiong Deng, Cyrus Shahabi, Ugur Demiryurek, and Yaguang Li. Price-aware real-time ride-sharing at scale: An auction-based approach. In Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS '16, pages 3:1--3:10, 2016.

Digital Library

[3]

Siddhartha Banerjee, Ramesh Johari, and Carlos Riquelme. Pricing in ride-sharing platforms: A queueing-theoretic approach. In Proceedings of the Sixteenth ACM Conference on Economics and Computation, EC '15, pages 639--639, 2015.

Digital Library

[4]

Kostas Bimpikis, Ozan Candogan, and Daniela Saban. Spatial pricing in ride-sharing networks. https://papers.ssrn.com/abstract=2868080, 2016.

[5]

Gérard P. Cachon, Kaitlin M. Daniels, and Ruben Lobel. The role of surge pricing on a service platform with self-scheduling capacity. Manufacturing & Service Operations Management, 19(3):368--384, 2017.

Digital Library

[6]

Juan Castillo, Daniel T. Knoepfle, and E. Glen Weyl. Surge pricing solves the wild goose chase. https://papers.ssrn.com/abstract=2890666, 2017.

[7]

Yiwei Chen and Ming Hu. Pricing and matching with forward-looking buyers and sellers. https://papers.ssrn.com/abstract=2859864, 2017.

[8]

Le Chen, Alan Mislove, and Christo Wilson. Peeking beneath the hood of uber. In Proceedings of the 2015 Internet Measurement Conference, IMC '15, pages 495--508, 2015.

Digital Library

[9]

M. Keith Chen. Dynamic pricing in a labor market: Surge pricing and flexible work on the uber platform. In Proceedings of the 2016 ACM Conference on Economics and Computation, EC '16, pages 455--455, 2016.

Digital Library

[10]

Shih-Fen Cheng, Duc Thien Nguyen, and Hoong Chuin Lau. Mechanisms for arranging ride sharing and fare splitting for last-mile travel demands. In Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, AAMAS '14, pages 1505--1506, 2014.

Digital Library

[11]

Meng-Fen Chiang, Tuan-Anh Hoang, and Ee-Peng Lim. Where are the passengers?: A grid-based gaussian mixture model for taxi bookings. In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL '15, pages 32:1--32:10, 2015.

Digital Library

[12]

Liran Einav, Chiara Farronato, and Jonathan Levin. Peer-to-peer markets. Technical report, National Bureau of Economic Research, August 2015.

[13]

Zhixuan Fang, Longbo Huang, and Adam Wierman. Prices and subsidies in the sharing economy. In Proceedings of the 26th International Conference on World Wide Web, WWW '17, pages 53--62, 2017.

Digital Library

[14]

Harish Guda and Upender Subramanian. Strategic surge pricing on on-demand service platforms. https://papers.ssrn.com/abstract=2895227, 2017.

[15]

Juho Hamari, Mimmi Sjöklint, and Antti Ukkonen. The sharing economy: Why people participate in collaborative consumption. J. Assoc. Inf. Sci. Technol., 67(9):2047--2059, September 2016.

Digital Library

[16]

Ramón Iglesias, Federico Rossi, Kevin Wang, David Hallac, Jure Leskovec, and Marco Pavone. Data-driven model predictive control of autonomous mobility-on-demand systems. In Proceedings of 2018 IEEE International Conference on Robotics and Automation, ICRA '18, 2017.

[17]

Nikolay Laptev, Jason Yosinski, Li Erran Li, and Slawek Smyl. Time-series extreme event forecasting with neural networks at uber. In Proceedings of the 34th International Conference on Machine Learning, ICML '17, 2017.

[18]

Prime time for drivers. https://help.lyft.com/hc/en-us/articles/115012926467. Accessed: 2018-01-15.

[19]

New york city neighborhoods. http://www1.nyc.gov/site/planning/data-maps/opendata.page#other. Accessed: 2017-11-30.

[20]

New york city taxi trips. http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml. Accessed: 2017-03-30.

[21]

Erhun Ozkan and Amy Ward. Dynamic matching for real-time ridesharing. 2017. https://papers.ssrn.com/abstract=2844451.

[22]

Engineering extreme event forecasting at uber with recurrent neural networks. https://eng.uber.com/neural-networks/. Accessed: 2018-02-21.

[23]

K. Zhao, D. Khryashchev, J. Freire, C. Silva, and H. Vo. Predicting taxi demand at high spatial resolution: Approaching the limit of predictability. In 2016 IEEE International Conference on Big Data, BigData '16, pages 833--842, Dec 2016.

[24]

Lingxue Zhu and Nikolay Laptev. Deep and confident prediction for time series at uber. In 2017 IEEE International Conference on Data Mining Workshops, ICDMW '17, pages 103--110, 2017.

Cited By

Horner HPazour JMitchell J(2024)Increasing driver flexibility through personalized menus and incentives in ridesharing and crowdsourced delivery platformsNaval Research Logistics (NRL)10.1002/nav.22212Online publication date: 5-Jul-2024
https://doi.org/10.1002/nav.22212
Hikima YAkagi YKohjima MKurashima TToda H(2023)Price and Time Proposal Optimization for Ride-Hailing Services Based on Individual UtilitiesTransactions of the Japanese Society for Artificial Intelligence10.1527/tjsai.38-1_C-M1338:1(C-M13_1-12)Online publication date: 1-Jan-2023
https://doi.org/10.1527/tjsai.38-1_C-M13
Chen FCai HWan H(2023)An Intelligent Framework to Maximize Individual Taxi Driver Income2023 Winter Simulation Conference (WSC)10.1109/WSC60868.2023.10407856(3717-3728)Online publication date: 10-Dec-2023
https://doi.org/10.1109/WSC60868.2023.10407856
Show More Cited By

Index Terms

ADAPT-pricing: a dynamic and predictive technique for pricing to maximize revenue in ridesharing platforms
1. Applied computing
  1. Operations research
    1. Transportation
2. Information systems
  1. World Wide Web
    1. Web applications
      1. Crowdsourcing
        Incentive schemes
      2. Electronic commerce

Recommendations

Spatio-temporal pricing for ridesharing platforms

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, ...
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 ...
Repeated Sales with Multiple Strategic Buyers
EC '17: Proceedings of the 2017 ACM Conference on Economics and Computation

In a market with repeated sales of a single item to a single buyer, prior work has established the existence of a zero revenue perfect Bayesian equilibrium in the absence of a commitment device for the seller. This counter-intuitive outcome is the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2018

655 pages

ISBN:9781450358897

DOI:10.1145/3274895

General Chairs:
Farnoush Banaei-Kashani
University of Colorado, Denver
,
Erik Hoel
Esri
,
Program Chairs:
Ralf Hartmut Güting
FernUniversität in Hagen, Germany
,
Roberto Tamassia
Brown University
,
Li Xiong
Emory University

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

SIGSPATIAL: ACM Special Interest Group on Spatial Information

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Caltrans
NSF

Conference

SIGSPATIAL '18

Sponsor:

SIGSPATIAL

SIGSPATIAL '18: 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 6 - 9, 2018

Washington, Seattle

Acceptance Rates

SIGSPATIAL '18 Paper Acceptance Rate 30 of 150 submissions, 20%;

Overall Acceptance Rate 220 of 1,116 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

17
Total Citations
View Citations
900
Total Downloads

Downloads (Last 12 months)187
Downloads (Last 6 weeks)12

Reflects downloads up to 26 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

Horner HPazour JMitchell J(2024)Increasing driver flexibility through personalized menus and incentives in ridesharing and crowdsourced delivery platformsNaval Research Logistics (NRL)10.1002/nav.22212Online publication date: 5-Jul-2024
https://doi.org/10.1002/nav.22212
Hikima YAkagi YKohjima MKurashima TToda H(2023)Price and Time Proposal Optimization for Ride-Hailing Services Based on Individual UtilitiesTransactions of the Japanese Society for Artificial Intelligence10.1527/tjsai.38-1_C-M1338:1(C-M13_1-12)Online publication date: 1-Jan-2023
https://doi.org/10.1527/tjsai.38-1_C-M13
Chen FCai HWan H(2023)An Intelligent Framework to Maximize Individual Taxi Driver Income2023 Winter Simulation Conference (WSC)10.1109/WSC60868.2023.10407856(3717-3728)Online publication date: 10-Dec-2023
https://doi.org/10.1109/WSC60868.2023.10407856
Gao YSong YWei XGao XChen G(2023)SACA: An End-to-End Method for Dispatching, Routing, and Pricing of Online Bus-BookingDatabase Systems for Advanced Applications10.1007/978-3-031-30678-5_23(303-313)Online publication date: 14-Apr-2023
https://doi.org/10.1007/978-3-031-30678-5_23
Wakazono YIshida SShiraishi Y(2023)Vehicle Allocation Algorithm Improving User Satisfaction in Ride-SharingAdvances in Engineering and Information Science Toward Smart City and Beyond10.1007/978-3-031-29301-6_6(123-140)Online publication date: 25-May-2023
https://doi.org/10.1007/978-3-031-29301-6_6
Huang JHuang LLiu MLi HTan QMa XCui JHuang D(2022)Deep Reinforcement Learning-based Trajectory Pricing on Ride-hailing PlatformsACM Transactions on Intelligent Systems and Technology10.1145/347484113:3(1-19)Online publication date: 3-Mar-2022
https://dl.acm.org/doi/10.1145/3474841
Jin JCheng PChen LLin XZhang W(2022)GridTuner: Reinvestigate Grid Size Selection for Spatiotemporal Prediction Models2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00094(1193-1205)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00094
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
Otaki KNishi TShiga TKashiwakura T(2022)Optimization-based Predictive Approach for On-Demand TransportationPRICAI 2022: Trends in Artificial Intelligence10.1007/978-3-031-20868-3_34(466-477)Online publication date: 4-Nov-2022
https://doi.org/10.1007/978-3-031-20868-3_34
Shi BCao ZLuo Y(2022)A Deep Reinforcement Learning Based Dynamic Pricing Algorithm in Ride-HailingDatabase Systems for Advanced Applications10.1007/978-3-031-00126-0_36(489-505)Online publication date: 8-Apr-2022
https://doi.org/10.1007/978-3-031-00126-0_36
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

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

Media

Figures

Other

Tables

View Table of Contents