Public transit systems play an important role in the alleviation of traffic congestion in urban road networks. The same vehicle type and a fixed departure timetable are usually applied to a bus route in the conventional public transit systems. They fail to cater to the time-varying travel demand or the diversified characteristics of transit passengers. To this end, this study proposes a demand-responsive public transit (DRPT) system consisting of a fixed bus route and demand-responsive stops with multiple vehicle types. The vehicle types of dispatched buses and the ride-matching schemes are optimized to serve transit passengers in real-time. Due to the non-convexity, Deep Q-Network (DQN), a reinforcement learning (RL) algorithm, is applied to the dynamic dispatching problem in the proposed DRPT system. The numerical studies validate the advantages of the proposed DRPT system and the RL-based dispatching algorithm.


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    Title :

    Reinforcement Learning Based Demand-Responsive Public Transit Dispatching


    Contributors:
    Wu, Mian (author) / Yu, Chunhui (author) / Ma, Wanjing (author) / Wang, Ling (author) / Ma, Xiaolong (author)

    Conference:

    21st COTA International Conference of Transportation Professionals ; 2021 ; Xi’an, China


    Published in:

    CICTP 2021 ; 387-398


    Publication date :

    2021-12-14




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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