Aiming at the problem of full quantitative analysis of railway accident causes, an improved HFACS model is proposed. Firstly, based on the original HFACS model, an improved HFACS model for railway industry was constructed Secondly, based on the improved model and association rule algorithm, the causative factors of 504 railway accidents from 2008 to 2009 collected by a railway bureau were comprehensively quantified, the association rule base of accident causative factors was mined, and the data of accident causative factors were visualized. The results show that the four main causes of railway accidents include irregularities, inadequate safety inspection, inadequate awareness of safety responsibility and inadequate education of safety responsibility. Based on the improved HFACS causative correlation analysis method, the importance of technical factors can be enhanced. Finally, the author puts forward some solutions to the lack of safety responsibility consciousness of the key accident factors.


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    Titel :

    Association Rules Mining for Railway Accident Causes Based on Improved HFACS


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Zeng, Xiaoqing (Herausgeber:in) / Xie, Xiongyao (Herausgeber:in) / Sun, Jian (Herausgeber:in) / Ma, Limin (Herausgeber:in) / Chen, Yinong (Herausgeber:in) / Zeng, Xiaoqing (Autor:in) / Lin, Haixiang (Autor:in) / Lu, Ran (Autor:in) / Min, Gu (Autor:in)

    Kongress:

    International Symposium for Intelligent Transportation and Smart City ; 2022 May 20, 2022 - May 21, 2022



    Erscheinungsdatum :

    28.04.2023


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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