To grasp the trend of safety conditions in the operation process of multisystem rail transit, and allocate transport capacity and maintenance resources reasonably, the managers should master accurate and comprehensive safety evaluation of regional rail transit system. A data-driven model for safety evaluation of regional rail transit system was proposed in this study. The deep autoencoder networks were employed to reduce the dimensions of the evaluation index system. The hybrid hierarchical k-means clustering method was applied to obtain the set of all possible safety status. The tree-augmented naïve Bayes algorithm was used to evaluate the overall safety. The validity and practicality of the model were verified using actual operations data from a rail transit network in regional urban agglomeration in China. A comparison with the actual situation shows that the proposed approach can evaluate the safety level of the network effectively.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Evaluating Regional Rail Transit Safety: A Data-Driven Approach


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Qin, Yong (Herausgeber:in) / Jia, Limin (Herausgeber:in) / Yang, Jianwei (Herausgeber:in) / Diao, Lijun (Herausgeber:in) / Yao, Dechen (Herausgeber:in) / An, Min (Herausgeber:in) / Li, Qing (Autor:in) / Liu, Ling (Autor:in) / Liu, Jun (Autor:in) / Zhang, Wanqiu (Autor:in) ... [mehr]

    Kongress:

    International Conference on Electrical and Information Technologies for Rail Transportation ; 2023 ; Beijing, China October 19, 2023 - October 21, 2023



    Erscheinungsdatum :

    03.02.2024


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Evaluating Regional Rail Transit Safety: A Matter-Element Analysis Method*

    LI, Qing / LIU, Ling / ZHANG, Wanqiu et al. | IEEE | 2020


    Wind-driven rail transit safety equipment

    CHEN PAI | Europäisches Patentamt | 2021

    Freier Zugriff


    Data-Driven Safety Model on Urban Rail Transit Signal System

    Chen, Yujia / Zeng, Xiaoging / Yuan, Tengfei | TIBKAT | 2019


    Data-Driven Safety Model on Urban Rail Transit Signal System

    Chen, Yujia / Zeng, Xiaoqing / Yuan, Tengfei | Springer Verlag | 2019