In the US, heavy-vehicles (a truck with a gross vehicle weight rating of greater than 10,000 pounds) moved 73.1% of all freight by value, 71.3% by weight, and 42.0% by ton-miles. As these values are expected to increase through 2040, safety regarding heavy-vehicles will be a concern for transportation engineers, transportation planners, safety agencies, federal agencies, and state agencies. The present study seeks to identify heavy-vehicle driver injury severity contributing factors by time-of-week in Minnesota (weekday crashes and weekend crashes) through a mixed logit modeling framework that accounts for the unobserved factors commonly present in crash data (this is typically referred to as unobserved heterogeneity). Data used for analysis consisted of heavy-vehicle crashes that occurred in Minnesota from 2004 to 2014. Each crash file (accident, vehicle, and occupant) were merged. The accident file and vehicle file were merged based on the variable "caseno," then that file was merged once more based on the variables "caseno" and "vehno."


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    Titel :

    Heavy-Vehicle Driver Injury Severity Analysis by Time of Week: A Mixed Logit Approach Using HSIS Crash Data


    Beteiligte:

    Erschienen in:

    ITE journal ; 87 , 9


    Erscheinungsdatum :

    2017



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch



    Klassifikation :

    BKL:    55.00 Technik der Verkehrsmittel, Verkehrswesen: Allgemeines
    Lokalklassifikation TIB:    770/7010