A value-based dynamic learning approach for vehicle dispatch in ride-sharing

Cheng Li, David Parker, Qi Hao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

To ensure real-time response to passengers, existing solutions to the vehicle dispatch problem typically optimize dispatch policies using small batch windows and ignore the spatial-temporal dynamics over the long-term horizon. In this paper, we focus on improving the long-term performance of ride-sharing services and propose a deep reinforcement learning based approach for the ride-sharing dispatch problem. In particular, this work includes: (1) an offline policy evaluation (OPE) based method to learn a value function that indicates the expected reward of a vehicle reaching a particular state; (2) an online learning procedure to update the offline trained value function to capture the real-time dynamics during the operation; (3) an efficient online dispatch method that optimizes the matching policy by considering both past and future influences. Extensive simulations are conducted based on New York City taxi data, and show that the proposed solution further increases the service rate compared to the state-of-the-art farsighted ride-sharing dispatch approach.
Original languageEnglish
Title of host publication2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)
PublisherIEEE
Pages11388-11395
Number of pages8
ISBN (Electronic)978-1-6654-7927-1
ISBN (Print)978-1-6654-7928-8
DOIs
Publication statusE-pub ahead of print - 26 Dec 2022
Event2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022 - Kyoto International Conference Center, Kyoto, Japan
Duration: 23 Oct 202227 Oct 2022
https://iros2022.org/

Publication series

NameIEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherIEEE
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
Abbreviated titleIROS 2022
Country/TerritoryJapan
CityKyoto
Period23/10/2227/10/22
Internet address

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