ABSTRACT Introduction Motorcycle passengers comprise a considerable proportion of traffic crash victims. During a 5year period (2006–2010) in Iran, an average of 3.4 pillion passengers are killed daily due to motorcycle crashes. This study investigated the main factors influencing crash severity of this group of road users. Method The Classification and Regression Trees (CART) method was employed to analyze the injury severity of pillion passengers in Iran over a 4year period (2009–2012). Results The predictive accuracy of the model built with a total of 16 variables was 74%, which showed a considerable improvement compared to previous studies. The results indicate that area type, land use, and injured part of the body (head, neck, etc.) are the most influential factors affecting the fatality of motorcycle passengers. Results also show that helmet usage could reduce the fatality risk among motorcycle passengers by 28%. Practical Applications The findings of this study might help develop more targeted countermeasures to reduce the death rate of motorcycle pillion passengers.

    Highlights area type, land use and the injured part of the body might be the most important variables influencing the pillion passenger fatalities crash severity is higher in rural areas compared with urban areas the fatality risk of pillion passenger is expected to reduce by 28% provided that all passengers wear helmets


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    Titel :

    A data mining approach to investigate the factors influencing the crash severity of motorcycle pillion passengers


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    2014-09-17


    Format / Umfang :

    6 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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