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Tytuł artykułu

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

Treść / Zawartość
Identyfikatory
Warianty tytułu
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.
Rocznik
Strony
6--24
Opis fizyczny
Bibliogr. 45 poz., rys., tab., wykr., wzory
Twórcy
  • Department of Civil & Architectural Engineering, University of Wyoming, 1000 E University Avenue, Laramie, WY 82071, USA
  • Wyoming Technology Transfer Center, University of Wyoming, 1000 E. University Ave., Rm 3029, Laramie, WY 82071, USA
  • Wyoming Technology Transfer Center, Department of Civil & Architectural Engineering, University of Wyoming, 1000 E University Avenue, Laramie, WY 82071, USA
Bibliografia
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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).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2b4fc5f3-2ea4-45a2-ac1b-c727907f4e62
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