Public transportation scheduling problem is a complex problem considering various influencing factors (such as passenger flow, etc.). However, the effect is not ideal when using the traditional machine learning method. Therefore,this paper aims to propose a Deep Reinforcement Learning Proximal Policy Optimization (DRL-PPO) algorithm for public transport scheduling according to different actual passenger flow. At the end of the paper, we make bus arrival time records and passenger OD data of Guangzhou route 560, for three months from July 1, 2019, to September 30, 2019 for training. Compared with GA algorithm, PPO algorithm is better than GA in convergence speed and final effect, which verifies the effectiveness of PPO in bus scheduling with complex passenger flow characteristics.


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    Titel :

    Deep Reinforcement Learning for Bus Scheduling Considering Complex Passenger Flow


    Beteiligte:
    Fu, Hui (Autor:in) / Chen, Niu (Autor:in) / Yao, Yipeng (Autor:in) / Han, Shuang (Autor:in) / Wang, Yefei (Autor:in)

    Kongress:

    22nd COTA International Conference of Transportation Professionals ; 2022 ; Changsha, Hunan Province, China


    Erschienen in:

    CICTP 2022 ; 326-335


    Erscheinungsdatum :

    08.09.2022




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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