Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform
Artikel i vetenskaplig tidskrift, 2022

The vehicle dispatching system is one of the most critical problems in online ride-hailing platforms, which requires adapting the operation and management strategy to the dynamics of demand and supply. In this paper, we propose a single-agent deep reinforcement learning approach for the vehicle dispatching problem called deep dispatching, by reallocating vacant vehicles to regions with a large demand gap in advance. The simulator and the vehicle dispatching algorithm are designed based on industrial-scale real-world data and the workflow of online ride-hailing platforms, ensuring the practical value of our approach. Besides, the vehicle dispatching problem is translated in analogy with the load balancing problem in computer networks. Inspired by the recommendation system, the problem of high concurrency of dispatching requests is addressed by sorting the actions as a recommendation list, whereby matching action with requests. Experiments demonstrate that the proposed approach is superior to existing benchmarks. It is also worth noting that the proposed approach won first place in the vehicle dispatching task of KDD Cup 2020.

Load balancing

Deep reinforcement learning

Vehicle dispatching


Yang Liu


Fanyou Wu

Purdue University

Cheng Lyu

Technische Universität München

Shen Li

Tsinghua University

Jieping Ye

University of Michigan

Xiaobo Qu

Tsinghua University

Transportation Research Part E: Logistics and Transportation Review

1366-5545 (ISSN)

Vol. 161 102694


Robotteknik och automation

Datavetenskap (datalogi)




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