This study applies the random parameter logit model (RPL), random parameter logit model with mean and variance heterogeneity (RPLMV), random forest (RF), and extreme random tree (ERT) to investigate the factors influencing the severity of both freeway and non-freeway crashes. Furthermore, it aims to analyze the disparities between logit models and tree-based machine learning models in terms of their predictive performance and interpretability. The findings of this research contribute to a better comprehension of the disparities in prediction accuracy and interpretive capabilities between logit models and machine learning models. Moreover, they provide insights into the selection and application of computational SHAP methods. Additionally, the outcomes can serve as valuable references for governmental bodies and organizations in formulating policies.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Analysis of injury severity in freeway and non-freeway crashes based on logit model and machine learning


    Beteiligte:
    Liu, Wei (Autor:in) / Zhou, Tuqiang (Autor:in)


    Erscheinungsdatum :

    2023-08-04


    Format / Umfang :

    1124151 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Classifying features of freeway crashes using machine learning

    Najafi, Zahra / Sadeghi, Rasool / Arghami, Shirazeh | Taylor & Francis Verlag | 2022



    Analysis of Crashes on Freeway Weaving Sections

    Mallipaddi, Venkata / Anderson, Michael | ASCE | 2020