The crashes involving HAZMAT vehicles can easily lead to serious social impact. The results from many researches have proved that risk assessment of transportation procedure was one effective approach for safety improvement. As a result, quantitative risk modeling methods like driving scale, driving rating, usage based insurance model have been popularly developed, but all the above mentioned methods were mainly implemented in passenger vehicles. Therefore, the present study attempt to investigate the HAZMAT transportation risk modeling by integrating three factors as mileage traveled, driving time and speeding behavior using the naturalistic transportation vehicle driving data. Thirty-nine HAZMAT transportation vehicles were installed with on-board diagnostic (OBD) vehicle data recording systems and Advanced Driving Assistance Systems (ADAS) with function delivering driving data to vehicle monitoring center through the cellular network. Two months of driving data including the variable of the mileage, time, and speeding were processed for risk assessment modeling. Analytic Hierarchy Process (AHP) method and Entropy Weight (EW) method was adopted to determine the weight of each variable. Comparison of the rating results for each vehicle driver indicated that, their rated risk level was consistent for different months. The frequency of safety-related events was significantly negative correlated with the rating scores. All these findings have demonstrated that the model proposed in this study was reliable for risk prediction for HAZMAT commercial transportation. The operation of commercial transportation could reduce the high-risk by adjusting the mileage traveled, driving time and enhance enforcement on speeding behavior.


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    Titel :

    Mileage Traveled, Driving Time, and Speeding Behavior as Predictors for Hazmat Transportation Risk Assessment Using Naturalistic Driving Data


    Beteiligte:
    Li, Siyao (Autor:in) / Liu, Nanhua (Autor:in) / Zhang, Hui (Autor:in) / Wu, Chaozhong (Autor:in)


    Erscheinungsdatum :

    2019-07-01


    Format / Umfang :

    282062 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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