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Identification of Critical Track Sections in a Railway Station Using a Multiplex Networks Approach

Author

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  • Pengfei Gao

    (School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China)

  • Wei Zheng

    (School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China
    National Research Center of Railway Safety Assessment, Beijing Jiaotong University, Beijing 100044, China)

  • Jintao Liu

    (School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China)

  • Daohua Wu

    (School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Railway stations serve as critical nodes within transportation networks, and the efficient management of in-station track sections is vital for smooth operations. This study proposes an integrated method for identifying critical track sections, which refers to track sections with the highest static occupancy rates (HiSORTS), in railway station yards using a multiplex network framework. By modeling the station as a Railway Station Multiplex Network (RSMN) that incorporates train routes (TRs), extended routes (ERs), and shunting routes (SRs), the proposed approach overcomes the limitations of single-layer, single-metric analyses and effectively captures complex operational characteristics. Classical network metrics, including Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Katz Centrality (KC), and PageRank (PR), along with a custom Fusion Centrality (FC), are used to quantify track section importance. Principal Component Analysis (PCA) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are applied to generate rankings, which are further analyzed using SHapley Additive exPlanations (SHAP)-based matrics contributions analysis. The results indicate that TR metrics contribute the most (50.3%), followed by ER (25.5%) and SR (24.2%), with KC and FC being the most influential metrics. The findings provide a robust decision-support framework for railway operations, facilitating targeted maintenance, congestion mitigation, and efficiency optimization.

Suggested Citation

  • Pengfei Gao & Wei Zheng & Jintao Liu & Daohua Wu, 2025. "Identification of Critical Track Sections in a Railway Station Using a Multiplex Networks Approach," Mathematics, MDPI, vol. 13(7), pages 1-31, March.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:7:p:1151-:d:1624959
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    References listed on IDEAS

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