A Value-based Dynamic Learning Approach for Vehicle Dispatch in Ride-Sharing

Cheng Li1; David Parker2; Qi Hao3

10.1109/IROS47612.2022.9981216

2022

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

2153-0858

978-1-6654-7928-8

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

11388-11395

23-27 Oct. 2022

Kyoto, Japan

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

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 far-sighted ride-sharing dispatch approach.

Learning systems Deep learning Urban areas Reinforcement learning Real-time systems Data models Vehicle dynamics

First

English

CPCI-S

Shenzhen Fundamental Research Program[JCYJ20200109141622964] ; Intel ICRI-IACV Research Fund[52514373]

Automation & Control Systems ; Computer Science ; Engineering ; Robotics

Automation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Robotics

WOS:000909405303045

IEEE

1.Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
2.School of Computer Science, University of Birmingham, Birmingham, UK
3.Research Institute of Trustworthy Autonomous System, Southern University of Science and Technology, Shenzhen, China

Cheng Li,David Parker,Qi Hao. A Value-based Dynamic Learning Approach for Vehicle Dispatch in Ride-Sharing[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2022:11388-11395.