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

• Autorzy: Ampadu Vincent-Michael Kwesi , Haq Muhammad Tahmidul , Ksaibati Khaled

Identyfikatory

DOI

10.14254/jsdtl.2022.7-2.1

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

crash severity; performance; extreme gradient boosting tree; adaptive boosting tree; random forest; gradient boost decision tree; adaptive synthetic algorithm

• Wydawca: Fundacja Centrum Badań Socjologicznych ; Scientific Publishing House "SciView"
• Czasopismo: Journal of Sustainable Development of Transport and Logistics
• Rocznik: 2022
• Tom: Vol. 7, No. 2
• Strony: 6--24
• Opis fizyczny: Bibliogr. 45 poz., rys., tab., wykr., wzory

Twórcy

autor

Ampadu Vincent-Michael Kwesi

• Department of Civil & Architectural Engineering, University of Wyoming, 1000 E University Avenue, Laramie, WY 82071, USA

• https://orcid.org/0000-0002-3635-3428

autor

Haq Muhammad Tahmidul

• Wyoming Technology Transfer Center, University of Wyoming, 1000 E. University Ave., Rm 3029, Laramie, WY 82071, USA

• https://orcid.org/0000-0003-3307-288X

autor

Ksaibati Khaled

• Wyoming Technology Transfer Center, Department of Civil & Architectural Engineering, University of Wyoming, 1000 E University Avenue, Laramie, WY 82071, USA

• https://orcid.org/0000-0002-9241-1792

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Uwagi

1. The authors would like to acknowledge that this work is part of a funded project by the Wyoming Department of Transportation (W.Y.D.O.T., contract no. RS09126) and Mountain-Plains Consortium (MPC-540). All Figures, tables, and equations listed in this paper will be included in the final report at the conclusion of this study.

2. Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).