This study involved the investigation of various machine learning methods, including four classification tree-based ML models, namely the Adaptive Boosting tree, Random Forest, Gradient Boost Decision Tree, Extreme Gradient Boosting tree, and three non-tree-based ML models, namely Support Vector Machines, Multi-layer Perceptron and k-Nearest Neighbors for predicting the level of severity of large truck crashes on Wyoming road networks. The accuracy of these seven methods was then compared. The Final ROC AUC score for the optimized random forest model is 95.296 %. The next highest performing model was the k-NN with 92.780 %, M.L.P. with 87.817 %, XGBoost with 86.542 %, Gradboost with 74.824 %, SVM with 72.648 % and AdaBoost with 67.232 %. Based on the analysis, the top 10 predictors of severity were obtained from the feature importance plot. These may be classified into whether safety equipment was used, whether airbags were deployed, the gender of the driver and whether alcohol was involved.


    Access

    Download


    Export, share and cite



    Title :

    An assessment of machine learning and data balancing techniques for evaluating downgrade truck crash severity prediction in Wyoming


    Contributors:


    Publication date :

    2022




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown





    Evaluating the Safety Effectiveness of Advance Downgrade Warning Signs in Preventing Downgrade Truck Crashes using a Propensity Scores Framework

    Moomen, Milhan / Rezapour, Mahdi / Raja, Mustaffa N. et al. | Transportation Research Record | 2019


    Effective signing to reduce truck downgrade runaways

    Stein,A.C. / Johnson,W.A. / Systems Technol.,Hawthorne,US | Automotive engineering | 1984


    Freeway Truck Traffic Safety in Wyoming: Crash Characteristics and Prediction Models

    Haq, Muhammad Tahmidul / Zlatkovic, Milan / Ksaibati, Khaled | Transportation Research Record | 2019


    Developing Truck Corridor Crash Severity Index

    Qin, Xiao | Online Contents | 2013