This study aims at analyzing the factors of aberrant driving behaviors and road accidents among Chinese ride-hailing drivers. Four hundred and twenty ride-hailing drivers (Male = 65%) completed a web-based questionnaire survey that assessed personal attributes, work-condition factors, aberrant driving behaviors, and self-reported road accidents in the last three years. A 10-item violations Driver Behavior Questionnaire (DBQ) scale was adopted to explore the aberrant driving behaviors of ride-hailing drivers. The ordinal regression model was used to examine the effects of personal attributes and work-condition factors on aberrant driving behaviors. A binary logistic regression model was employed to investigate the predictors of road accidents. The descriptive statistics indicate that among ride-hailing drivers, the traditional taxi drivers were found to be more involved in aberrant driving behaviors than private car drivers. The results from the Principal Component Analysis (PCA) reveal that ride-hailing drivers were involved in "risky violations." Male and young ride-hailing drivers were found to be more involved in risky violations than their counterparts. Furthermore, it is revealed that a one-unit increase in risky violations increased the probability of being involved in road accidents by 60%. Furthermore, a one-unit increase in work-condition factors increased the likelihood of being involved in road accidents by 41%. The findings in this study can help better understand the aberrant driving behaviors of ride-hailing drivers and contribute to a more effective policy for reducing the road accidents caused by ride-hailing drivers.


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    Titel :

    A study of aberrant driving behaviors and road accidents in Chinese ride-hailing drivers


    Beteiligte:
    Shi, Jing (Autor:in) / Hussain, Muhammad (Autor:in) / Peng, Dandan (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    02.09.2023


    Format / Umfang :

    18 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




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