In metro system, unpredictable disturbances influence the normal operation and bring much inconvenience to passengers. This paper focuses on train timetable rescheduling (TTR) problem with considering practical operations in the metro management. At first, an optimization model that takes the deviation of rescheduled timetable, the total delay time of passengers and energy consumption as objective is developed. Meanwhile, the constraints, together with some practical rescheduling rules (e.g., the preprogrammed speed profiles, train detention strategy) are introduced. Secondly, the model is reformulated into an Markov decision process (MDP) with well defining the state, action and reward function, which is then solved by the proposed Q-learning approach. Finally, some case studies using the operational data of Beijing Yizhuang Subway Line are carried out to demonstrate the effectiveness of the proposed approach. The results indicate that a tradeoff solution among the optimization objectives can be obtained within a short time.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Metro Train Timetable Rescheduling Based on Q-learning Approach


    Contributors:
    Su, Boyi (author) / Wang, Zhikai (author) / Su, Shuai (author) / Tang, Tao (author)


    Publication date :

    2020-09-20


    Size :

    467183 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Collaborative optimization of rescue operation and timetable rescheduling under metro train failure

    Zhu, Qiaozhen / Bai, Yun / Yang, Lingling et al. | Taylor & Francis Verlag | 2023




    Integrated Train Timetable and Vehicles Rescheduling Based on MPC

    Zhao, Zhi / Yue, Yixiang / Wang, Yongcheng | Springer Verlag | 2024