Road crashes are a common occurrence in many parts of the world, causing significant loss of life, injury, and economic damage. Crashes can be broadly classified into single-vehicle (SV) crashes and multi-vehicle (MV) crashes. Various statistical approaches have been implemented to identify the key factors behind these two types of crashes and it has been concluded that these factors need to be analyzed separately. The dataset for this research included various types of roadway design parameters and traffic conditions. Combinations of three feature-selection techniques, namely ANOVA, correlation matrix, and ExtraTreesClassifier algorithm, were utilized to separately select the appropriate variables for SV and MV crash analysis. Various machine learning (ML) models (e.g., LightGBM, XGBoost, etc.) along with a statistical method (binary logistic regression) have been adopted to predict SV and MV crash occurrences. The results show that gradient boosting-type ML algorithms outperform the remaining prediction models, and the LightGBM was found to be the most powerful in prediction. The LightGBM classifier produced accuracy, ROC_AUC, and avg. F-1 score of 0.75, 0.83, and 0.76, respectively, for MV crashes and 0.76, 0.82, and 0.76, respectively, for SV crashes. The SHapley Additive exPlanations (SHAP) analysis was used to explain how each variable affected the models’ output. The results confirmed that the crash factors associated with SV and MV crashes are different and that some variables have inverse impact. Artificial intelligence and ML can assist transportation professionals in better understanding the causes of SV and MV crashes and advance the process toward Vision Zero.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Leveraging Machine Learning Algorithms to Predict and Analyze Single-Vehicle and Multi-Vehicle Crash Occurrences on Motorways


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board




    Publication date :

    2024-06-06




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Crash Prediction Models for Rural Motorways

    Montella, Alfonso / Colantuoni, Lucio / Lamberti, Renato | Transportation Research Record | 2008


    Crash Prediction Models for Rural Motorways

    Montella, Alfonso | Online Contents | 2008


    Motion Based Vehicle Detection on Motorways

    Gillner, W. / IEEE / Industrial Electronics Society | British Library Conference Proceedings | 1995


    Vehicle fires on motorways in 1969

    Chandler, S. E. | TIBKAT | 1972


    Analyze and Predict Car Accidents Using Different Machine Learning Algorithms

    Harimanto, Farrell Putra / Andrew, Chris / William, Henry et al. | IEEE | 2023