The fatal or incapacitating injury caused by overturn crashes involving sport utility vehicles (SUVs) is irreparable. The purpose of this study is to identify potential factors that affect the injury severity of overturn crashes involving SUVs and develop adequate preventive strategies. Given the unobserved heterogeneity existing in the data set, crash data in North Carolina from Highway Safety Information System (HSIS) is analyzed and separated by Latent Class Clustering into six relatively homogeneous groups. To further explore the heterogeneity, random parameter logit models are developed for each cluster, and the impacts of significant factors are estimated with marginal effects. The results reveal the heterogeneity across the clusters and the homogeneity within the same cluster. Variables (including females, people over fifty years old, improper or aggressive behavior, rural areas, high-speed limit, curved roadway, rolling and mountainous terrain, adverse weather, and poor light conditions) are associated with the injury severity of the overturn crashes involving SUVs. The findings of this study can further provide decision makers with insightful countermeasures to improve transportation safety and mitigate the injuries of overturn crashes involving SUVs.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Analyzing the Injury Severity in Overturn Crashes Involving Sport Utility Vehicles: Latent Class Clustering and Random Parameter Logit Model


    Additional title:

    J. Transp. Eng., Part A: Systems


    Contributors:
    Hua, Chengying (author) / Fan, Wei (author) / Song, Li (author) / Liu, Shaojie (author)


    Publication date :

    2023-03-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Indicators of injury severity of truck crashes using random parameter logit modeling

    Ali Rabbani, Muhammad Babar / Usama, Umar / Musarat, Muhammad Ali et al. | IEEE | 2021



    A latent class multinomial logit analysis of factors associated with pedestrian injury severity of inter-urban highway crashes

    Agyemang, William / Adanu, Emmanuel Kofi / Liu, Jun et al. | Taylor & Francis Verlag | 2023


    Bicyclist injury severity classification using a random parameter logit model

    Subasish Das / Reuben Tamakloe / Hamsa Zubaidi et al. | DOAJ | 2023

    Free access