Although much research work for the operation safety has been taken in the railway domain, some accidents still occur because past experiences of accident analysis were not fully accumulated for safety improvement. This study aims to identify potential causal relationships among the many factors playing a role in railway accidents. A new interestingness measure, Confidence_interestingness ("C_" Inter) and corresponding improved algorithm, Positive and Negative Association Rules Algorithm based on "C_" Inter (PNARA_CI) were put forward in our study. Compared with traditional association rule mining algorithms, the PNARA_CI does not generate candidate association rules by means of frequent itemsets, but by the combination between every two accident factors, which can mine the positive and negative association rules with practical value to the maximum. And they were applied to railway accidents data to explore the association rules of the causal factors in the case study. The effectiveness of the algorithm was verified.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Factors correlation mining on railway accidents using association rule learning algorithm


    Beteiligte:
    Wang, Yakun (Autor:in) / Zheng, Wei (Autor:in) / Dong, Hairong (Autor:in) / Gao, Pengfei (Autor:in)


    Erscheinungsdatum :

    2020-09-20


    Format / Umfang :

    315403 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Railway accidents

    Gu¨ / nther, K. | Engineering Index Backfile | 1925


    Railway accidents

    Engineering Index Backfile | 1934


    Railway accidents

    Engineering Index Backfile | 1930


    Railway accidents

    Engineering Index Backfile | 1932