IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v161y2022ics1366554522000862.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554522000862
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2022.102694?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    3. 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).
    4. 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).
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    9. Long He & Zhenyu Hu & Meilin Zhang, 2020. "Robust Repositioning for Vehicle Sharing," Manufacturing & Service Operations Management, INFORMS, vol. 22(2), pages 241-256, March.
    10. 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.
    11. 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.
    12. Rahul Nair & Elise Miller-Hooks, 2011. "Fleet Management for Vehicle Sharing Operations," Transportation Science, INFORMS, vol. 45(4), pages 524-540, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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).
    3. 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.
    4. 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.
    5. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    6. Nie, Fapeng & Li, Xiang & Zhou, Chang, 2024. "Impact of privacy regulation involving information collection on the ride-hailing market," Omega, Elsevier, vol. 126(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Beojone, Caio Vitor & Geroliminis, Nikolas, 2023. "A dynamic multi-region MFD model for ride-sourcing with ridesplitting," Transportation Research Part B: Methodological, Elsevier, vol. 177(C).
    2. Yiling Zhang & Mengshi Lu & Siqian Shen, 2021. "On the Values of Vehicle-to-Grid Electricity Selling in Electric Vehicle Sharing," Manufacturing & Service Operations Management, INFORMS, vol. 23(2), pages 488-507, March.
    3. Martin, Layla & Minner, Stefan, 2021. "Feature-based selection of carsharing relocation modes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    4. Yang, Yu & Ridouane, Yassine & Boland, Natashia & Erera, Alan & Savelsbergh, Martin, 2022. "Substitution-based equipment balancing in service networks with multiple equipment types," European Journal of Operational Research, Elsevier, vol. 300(3), pages 966-978.
    5. Fu, Chenyi & Ma, Shoufeng & Zhu, Ning & He, Qiao-Chu & Yang, Hai, 2022. "Bike-sharing inventory management for market expansion," Transportation Research Part B: Methodological, Elsevier, vol. 162(C), pages 28-54.
    6. Golalikhani, Masoud & Oliveira, Beatriz Brito & Carravilla, Maria Antónia & Oliveira, José Fernando & Antunes, António Pais, 2021. "Carsharing: A review of academic literature and business practices toward an integrated decision-support framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    7. Saif Benjaafar & Daniel Jiang & Xiang Li & Xiaobo Li, 2022. "Dynamic Inventory Repositioning in On-Demand Rental Networks," Management Science, INFORMS, vol. 68(11), pages 7861-7878, November.
    8. Ke, Jintao & Wang, Ce & Li, Xinwei & Tian, Qiong & Huang, Hai-Jun, 2024. "Equilibrium analysis for on-demand food delivery markets," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).
    9. Shetty, Akhil & Li, Sen & Tavafoghi, Hamidreza & Qin, Junjie & Poolla, Kameshwar & Varaiya, Pravin, 2022. "An analysis of labor regulations for transportation network companies," Economics of Transportation, Elsevier, vol. 32(C).
    10. Long He & Guangrui Ma & Wei Qi & Xin Wang, 2021. "Charging an Electric Vehicle-Sharing Fleet," Manufacturing & Service Operations Management, INFORMS, vol. 23(2), pages 471-487, March.
    11. 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).
    12. Du, Muqing & Zhou, Jiankun & Chen, Anthony & Tan, Heqing, 2022. "Modeling the capacity of multimodal and intermodal urban transportation networks that incorporate emerging travel modes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    13. Hu, Lu & Liu, Yang, 2016. "Joint design of parking capacities and fleet size for one-way station-based carsharing systems with road congestion constraints," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 268-299.
    14. Fu, Chenyi & Zhu, Ning & Ma, Shoufeng & Liu, Ronghui, 2022. "A two-stage robust approach to integrated station location and rebalancing vehicle service design in bike-sharing systems," European Journal of Operational Research, Elsevier, vol. 298(3), pages 915-938.
    15. Gong, Manlin & Hu, Yucong & Chen, Zhiwei & Li, Xiaopeng, 2021. "Transfer-based customized modular bus system design with passenger-route assignment optimization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    16. Shi, Ziyi & Xu, Meng & Song, Yancun & Zhu, Zheng, 2024. "Multi-Platform dynamic game and operation of hybrid Bike-Sharing systems based on reinforcement learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).
    17. Zhong, Yuanguang & Zillmann, Stefan & Zhang, Ruijie & Zhou, Yong-Wu & Xie, Wei, 2023. "Vehicle repositioning for a ride-sourcing network system providing differentiated services," Transportation Research Part B: Methodological, Elsevier, vol. 170(C), pages 221-243.
    18. Sumitkumar, Rathor & Al-Sumaiti, Ameena Saad, 2024. "Shared autonomous electric vehicle: Towards social economy of energy and mobility from power-transportation nexus perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).
    19. Vignon, Daniel & Yin, Yafeng & Ke, Jintao, 2023. "Regulating the ride-hailing market in the age of uberization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    20. Gao, Dong Li & Xie, Wei & Ming Lee, Eric Wai, 2022. "Individual-level exit choice behaviour under uncertain risk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transe:v:161:y:2022:i:c:s1366554522000862. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.