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

Vincent Michael Ampadu (1) , Muhammad Tahmidul Haq (2) , Khaled Ksaibati (3)
(1) Department of Civil & Architectural Engineering, University of Wyoming, 1000 E University Avenue, Laramie, WY 82071, USA , United States
(2) Wyoming Technology Transfer Center, University of Wyoming, 1000 E. University Ave., Rm 3029, Laramie, WY 82071, USA , United States
(3) Wyoming Technology Transfer Center, Department of Civil & Architectural Engineering, University of Wyoming, 1000 E University Avenue, Laramie, WY 82071, USA , United States


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.

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Vincent Michael Ampadu (Primary Contact)
Muhammad Tahmidul Haq
Khaled Ksaibati
Ampadu, V. M., Haq, M. T., & Ksaibati, K. (2022). An assessment of machine learning and data balancing techniques for evaluating downgrade truck crash severity prediction in Wyoming. Journal of Sustainable Development of Transport and Logistics, 7(2), 6–24.

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