Highlights Abstracting the vehicle dispatching problem as a load balancing problem. Solving the challenge of concurrent requests with the help of recommendation system. Designing the DRL method based on the data of a real ride-hailing platform.

    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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:
    Liu, Yang (Autor:in) / Wu, Fanyou (Autor:in) / Lyu, Cheng (Autor:in) / Li, Shen (Autor:in) / Ye, Jieping (Autor:in) / Qu, Xiaobo (Autor:in)


    Erscheinungsdatum :

    2022-03-27




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Systems and methods for dispatching shared rides through ride-hailing platform

    LI SHUAIJI / TANG XIAOCHENG / QIN ZHIWEI | Europäisches Patentamt | 2023

    Freier Zugriff

    SYSTEMS AND METHODS FOR DISPATCHING SHARED RIDES THROUGH RIDE-HAILING PLATFORM

    LI SHUAIJI / TANG XIAOCHENG / QIN ZHIWEI | Europäisches Patentamt | 2022

    Freier Zugriff

    BM-DDPG: An Integrated Dispatching Framework for Ride-Hailing Systems

    Gao, Jie / Li, Xiaoming / Wang, Chun et al. | IEEE | 2022


    Real-world ride-hailing vehicle repositioning using deep reinforcement learning

    Jiao, Yan / Tang, Xiaocheng / Qin, Zhiwei (Tony) et al. | Elsevier | 2021


    Order-Dispatching Strategy Induced by Optimal Transport Plan for an Online Ride-Hailing System

    Lei, Dechao / Wu, Yuanshan | Transportation Research Record | 2022