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Article

Traffic Crash Severity Prediction—A Synergy by Hybrid Principal Component Analysis and Machine Learning Models

Civil & Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Int. J. Environ. Res. Public Health 2020, 17(20), 7598; https://doi.org/10.3390/ijerph17207598
Received: 25 September 2020 / Revised: 14 October 2020 / Accepted: 17 October 2020 / Published: 19 October 2020
(This article belongs to the Collection Driving Behaviors and Road Safety)
The accurate prediction of road traffic crash (RTC) severity contributes to generating crucial information, which can be used to adopt appropriate measures to reduce the aftermath of crashes. This study aims to develop a hybrid system using principal component analysis (PCA) with multilayer perceptron neural networks (MLP-NN) and support vector machines (SVM) in predicting RTC severity. PCA shows that the first nine components have an eigenvalue greater than one. The cumulative variance percentage explained by these principal components was found to be 67%. The prediction accuracies of the models developed using the original attributes were compared with those of the models developed using principal components. It was found that the testing accuracies of MLP-NN and SVM increased from 64.50% and 62.70% to 82.70% and 80.70%, respectively, after using principal components. The proposed models would be beneficial to trauma centers in predicting crash severity with high accuracy so that they would be able to prepare for appropriate and prompt medical treatment. View Full-Text
Keywords: traffic crash severity; vehicle crashes; emergency management; principal component analysis (PCA); neural networks (NN); support vector machine (SVM) traffic crash severity; vehicle crashes; emergency management; principal component analysis (PCA); neural networks (NN); support vector machine (SVM)
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MDPI and ACS Style

Assi, K. Traffic Crash Severity Prediction—A Synergy by Hybrid Principal Component Analysis and Machine Learning Models. Int. J. Environ. Res. Public Health 2020, 17, 7598. https://doi.org/10.3390/ijerph17207598

AMA Style

Assi K. Traffic Crash Severity Prediction—A Synergy by Hybrid Principal Component Analysis and Machine Learning Models. International Journal of Environmental Research and Public Health. 2020; 17(20):7598. https://doi.org/10.3390/ijerph17207598

Chicago/Turabian Style

Assi, Khaled. 2020. "Traffic Crash Severity Prediction—A Synergy by Hybrid Principal Component Analysis and Machine Learning Models" International Journal of Environmental Research and Public Health 17, no. 20: 7598. https://doi.org/10.3390/ijerph17207598

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