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Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform

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  • Liu, Yang
  • Wu, Fanyou
  • Lyu, Cheng
  • Li, Shen
  • Ye, Jieping
  • Qu, Xiaobo

Abstract

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.

Suggested Citation

  • Liu, Yang & Wu, Fanyou & Lyu, Cheng & Li, Shen & Ye, Jieping & Qu, Xiaobo, 2022. "Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    • Handle: RePEc:eee:transe:v:161:y:2022:i:c:s1366554522000862
    DOI: 10.1016/j.tre.2022.102694
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    7. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    8. Nie, Fapeng & Li, Xiang & Zhou, Chang, 2024. "Impact of privacy regulation involving information collection on the ride-hailing market," Omega, Elsevier, vol. 126(C).
    9. Gao, Chang & Lin, Xi & He, Fang & Tang, Xindi, 2024. "Online relocating and matching of ride-hailing services: A model-based modular approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 188(C).
    10. Ao, Di & Lai, Zhijie & Li, Sen, 2024. "Control of dynamic ride-hailing networks with a mixed fleet of autonomous vehicles and for-hire human drivers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 189(C).
    11. Liu, Chengqi & Wang, Zelin & Liu, Zhiyuan & Huang, Kai, 2025. "Multi-Agent reinforcement learning framework for addressing Demand-Supply imbalance of Shared Autonomous Electric Vehicle," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 197(C).
    12. Lu Yang & Leishan Zhou & Hanxiao Zhou & Chang Han & Wenqiang Zhao, 2023. "A Lagrangian Method for Calculation of Passing Capacity on a Railway Hub Station," Mathematics, MDPI, vol. 11(6), pages 1-20, March.
    13. Tao Wang & Dayi Qu & Hui Song & Shouchen Dai, 2023. "A Hierarchical Framework of Decision Making and Trajectory Tracking Control for Autonomous Vehicles," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
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