This paper describes a reinforcement learning (RL) approach to train timetabling, which takes into account the characteristics of inter-city high speed railway lines in China. A potential advantage of the proposed approach over well-established mathematical programming approaches lies in that it does not rely heavily on domain expertise to define the various timetabling rules and strategies. Specifically, a discrete time Markov Decision Process (MDP) is established to model the studied problem, and a well-designed RL method is proposed to solve the problem, assuming that the fundamental information about the studied lines (minimum running times, headways, stopping patterns, etc.) is known. Four inter-city high speed railway lines that operate on the Beijing-Tianjin corridor are employed as a case study to test the performance of the proposed approach. The obtained results preliminarily demonstrate the effectiveness and applicability of the proposed approach.


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    Title :

    A Reinforcement Learning Approach to Train Timetabling for Inter-City High Speed Railway Lines


    Contributors:
    Guo, Yiwei (author)


    Publication date :

    2020-09-01


    Size :

    402923 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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