Crash severity is one of the most widely studied topics in traffic safety area. Scholars have studied crash severity through various types of models. Using the publicly available 2017 Maryland crash data from the Department of Maryland State Police, the authors develop a multinomial logit (MNL) model and a random forest (RF) model, which belong to discrete choice and tree-based models, respectively, to (1) identify factors contributing to crash severity and (2) compare prediction performances and interpretation abilities between the two models. Based on the model results, major contributing factors of crash severity are identified, including collision type, occupant age, and speed limit. For the given dataset, RF has a higher prediction accuracy than MNL based on multiple measures (precision, recall, and F1 score), even though the differences are not dramatic. Sensitivity analysis results show that RF is less sensitive than MNL. RF can automatically capture the non-linear effects of continuous variables and reduce the influence of collinearity relationships existing among explanatory variables. This study shows the possibility of conducting sensitivity analysis to enhance understanding of MNL and RF results, and uncovers unique characteristics of the discrete choice and tree-based models.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Prediction and Factor Identification for Crash Severity: Comparison of Discrete Choice and Tree-Based Models


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:
    Wang, Xinyi (author) / Kim, Sung Hoo (author)


    Publication date :

    2019-05-05




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Multilevel Discrete Outcome Modeling for Crash Severity: A Novel Approach for Crash Severity Models

    Hossain, Md Julfiker / Pais, Namitha / Ivan, John N. et al. | Transportation Research Record | 2024



    Real time traffic crash severity prediction tool

    RATROUT NEDAL / MANSOOR UMER / ALAM GULZAR | European Patent Office | 2022

    Free access

    Predicting pedestrian crash occurrence and injury severity in Texas using tree-based machine learning models

    Zhao, Bo / Zuniga-Garcia, Natalia / Xing, Lu et al. | Taylor & Francis Verlag | 2024