Based on the historical travel data provided by commercial driver monitoring systems, this study presents the analysis of contributing factors affecting hazardous materials (HAZMAT) truck safety. A comprehensive data set of HAZMAT truck travel records (n = 4,487), including driver characteristics, roadway characteristics, environmental characteristics, and temporal characteristics, was developed for the study period of July 1st to July 31st, 2020. A random parameter ordered probit model was established for investigating the HAZMAT truck travel risk levels, which were measured by three warning frequency categories: high risk (more than one warning), moderate risk (one warning), and low risk (zero warning). The results show that speed, acceleration, lighting conditions (dusk/dawn, dark-lighted, and dark-unlighted), visibility (less than 500 m and between 500 m and 1000 m), weather (rain and fog), and time of day (0:00–6:00) have statistically significant effects on the increase of high-risk probability.


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    Titel :

    Exploring HAZMAT Truck Risk Factors with Driver Monitoring Systems Data Using Random Parameter Ordered Probit Model


    Beteiligte:
    Sun, Ming (Autor:in) / Zhou, Ronggui (Autor:in) / Jiao, Chengwu (Autor:in)

    Kongress:

    22nd COTA International Conference of Transportation Professionals ; 2022 ; Changsha, Hunan Province, China


    Erschienen in:

    CICTP 2022 ; 1704-1714


    Erscheinungsdatum :

    2022-09-08




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch