Although the number of motorcycle crashes has trended downward in Japan, it is crucial to propose effective safety measures for single-vehicle motorcycle crashes due to their high fatality rate, especially for fixed-object collisions. However, previous studies did not extensively investigate multiple significant factors associated with single-vehicle motorcycle crashes. To fill this research gap, this study examines the environmental, road, and driver factors associated with single-vehicle motorcycle crashes considering crash type, including fixed-object collisions and self-skidding. The 2-year crash data national wide in Japan are used for empirical study. The association rules mining is applied to discover various factors associated with single-vehicle motorcycle crashes. The significant finding indicates 1) that the fixed-object collision fatality rate was four-time higher than the self-skidding fatality rate; roads with some attributes (i.e., car-only category, dry surface condition, curve alignment, speed limit more than 60 km/h, and segment location) have to be considered due to higher fatality rate for single-vehicle motorcycle crashes, 2) that there were factors, including road characteristics (surface condition, alignment, and road category), environment (weather and lighting), motorcyclists (age), associated with the fatality of fixed-object collision than self-skidding. Traffic safety professionals can use the findings of this study to implement some countermeasures to reduce fatality due to single-vehicle motorcycle crashes.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Examining the Factors Affecting Single-Vehicle Motorcycle Crashes Using Association Rules Mining: Case Study of Japan*


    Beteiligte:
    Zulherman, Dodi (Autor:in) / Yang, Jia (Autor:in) / Yokota, Yasunari (Autor:in)


    Erscheinungsdatum :

    08.10.2022


    Format / Umfang :

    355907 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch