Highlights The train operation control problem is modeled by graphs and solved from a data-driven perspective. The model can analyze and exploit past realization data to provide decision support for traffic control. The proposed control model is superior to other standard rules and methods. The model has the advantage of explainability compared to existing traffic control models.

    Abstract Railway operations are subject to deviations from the planned schedule, i.e., delays. In those situations, high-quality traffic control actions are needed to reduce the delays. Existing studies mainly used prescriptive techniques (e.g., mathematical programming, heuristics) to identify the best control action. These methods have limitations in the firm reliance on deterministic parameters prescriptively or normatively determined beforehand, and little understandability by the practitioners. These drawbacks hinder their acceptance in practice. This study exploits instead past realization data to provide decision support for traffic control. The realized data describe the traffic control actions taken by human controllers, and their effects; those latter are more complex than a linear sum of predetermined parameters. We use decision graphs to identify which traffic control action leads to the best solution, in terms of reduction of delays, based on the past performance of the same action in similar conditions. We are also able to explain the reasons and the factors that lead to each suggested action. We focus on the relevant case of merging stations, where multiple lines merge as one line, deciding the relative order between two consecutive trains. The method determines the stochastic effects of the two possible decisions at merge points, which allows for choosing the best one. Compared within the framework of realized data, the action suggested is the best out of a series of benchmarks, including simple rules and optimization, improving (reducing delays) approximately 11.7% on the common benchmarks. The variables with the highest impact on the utility are the length of the planned dwell time and the planned presence of an overtaking. The variables influencing the utility most are the actual delays of trains, the train type, and the order actually implemented.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Train traffic control in merging stations: A data-driven approach


    Contributors:
    Huang, Ping (author) / Li, Zhongcan (author) / Zhu, Yongqiu (author) / Wen, Chao (author) / Corman, Francesco (author)


    Publication date :

    2023-04-26




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    METHOD FOR TRAIN TRAFFIC CONTROLLING AT STATIONS

    POLEVOJ YURIJ IOSIFOVICH / GORELIK ALEKSANDR VLADIMIROVICH / MUKHIN LEONID VIKTOROVICH | European Patent Office | 2021

    Free access

    Punctuality of Train Traffic in Dutch Railway Stations

    Yuan, J. / Hansen, I. A. | ASCE | 2002


    Punctuality of Train Traffic in Dutch Railway Stations

    Yuan, J. / Hansen, I. A. / American Society of Civil Engineers | British Library Conference Proceedings | 2002


    Formalizing train control language: automating analysis of train stations

    Svendsen, A. / Moller-Pedersen, B. / Haugen, O. et al. | British Library Conference Proceedings | 2010


    TRAIN TRAFFIC CONTROL SYSTEM AND TRAIN TRAFFIC CONTROL METHOD

    TOKUMARU MAKOTO | European Patent Office | 2021

    Free access