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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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    1. Ma, Tai-Yu & Rasulkhani, Saeid & Chow, Joseph Y.J. & Klein, Sylvain, 2019. "A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 417-442.
    2. Gao, Kun & Sun, Lijun & Yang, Ying & Meng, Fanyu & Qu, Xiaobo, 2021. "Cumulative prospect theory coupled with multi-attribute decision making for modeling travel behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 1-21.
    3. Zhan, Xingbin & Szeto, W.Y. & Shui, C.S. & Chen, Xiqun (Michael), 2021. "A modified artificial bee colony algorithm for the dynamic ride-hailing sharing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    4. Long He & Zhenyu Hu & Meilin Zhang, 2020. "Robust Repositioning for Vehicle Sharing," Manufacturing & Service Operations Management, INFORMS, vol. 22(2), pages 241-256, March.
    5. Zhang, Kenan & Nie, Yu (Marco), 2021. "Inter-platform competition in a regulated ride-hail market with pooling," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 151(C).
    6. Li, Yuanyuan & Liu, Yang, 2021. "Optimizing flexible one-to-two matching in ride-hailing systems with boundedly rational users," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    7. Liu, Zhiyuan & Wang, Zewen & Cheng, Qixiu & Yin, Ruyang & Wang, Meng, 2021. "Estimation of urban network capacity with second-best constraints for multimodal transport systems," Transportation Research Part B: Methodological, Elsevier, vol. 152(C), pages 276-294.
    8. Liu, Yang & Zhang, Qi & Lyu, Cheng & Liu, Zhiyuan, 2021. "Modelling the energy consumption of electric vehicles under uncertain and small data conditions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 313-328.
    9. Ke, Jintao & Li, Xinwei & Yang, Hai & Yin, Yafeng, 2021. "Pareto-efficient solutions and regulations of congested ride-sourcing markets with heterogeneous demand and supply," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    10. Debjit Roy & Jennifer A. Pazour & René de Koster, 2014. "A novel approach for designing rental vehicle repositioning strategies," IISE Transactions, Taylor & Francis Journals, vol. 46(9), pages 948-967, September.
    11. Song, Dong-Ping & Earl, Christopher F., 2008. "Optimal empty vehicle repositioning and fleet-sizing for two-depot service systems," European Journal of Operational Research, Elsevier, vol. 185(2), pages 760-777, March.
    12. Rahul Nair & Elise Miller-Hooks, 2011. "Fleet Management for Vehicle Sharing Operations," Transportation Science, INFORMS, vol. 45(4), pages 524-540, November.
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    Cited by:

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    2. Xiangyu Huang & Yan Cheng & Jing Jin & Aiqing Kou, 2024. "Research on Dynamic Subsidy Based on Deep Reinforcement Learning for Non-Stationary Stochastic Demand in Ride-Hailing," Sustainability, MDPI, vol. 16(15), pages 1-25, July.
    3. Zhao, Zhonghao & Lee, Carman K.M. & Yan, Xiaoyuan & Wang, Haonan, 2024. "Reinforcement learning for electric vehicle charging scheduling: A systematic review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 190(C).
    4. 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.
    5. Hua, Shijia & Zeng, Wenjia & Liu, Xinglu & Qi, Mingyao, 2022. "Optimality-guaranteed algorithms on the dynamic shared-taxi problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    6. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    7. Nie, Fapeng & Li, Xiang & Zhou, Chang, 2024. "Impact of privacy regulation involving information collection on the ride-hailing market," Omega, Elsevier, vol. 126(C).
    8. 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).
    9. 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).
    10. Wang, Jiawei & Guo, Qinglai & Sun, Hongbin, 2024. "Planning approach for integrating charging stations and renewable energy sources in low-carbon logistics delivery," Applied Energy, Elsevier, vol. 372(C).

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