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

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  • Khaled Assi

    (Civil & Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

Abstract

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.

Suggested Citation

  • Khaled Assi, 2020. "Traffic Crash Severity Prediction—A Synergy by Hybrid Principal Component Analysis and Machine Learning Models," IJERPH, MDPI, vol. 17(20), pages 1-16, October.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:20:p:7598-:d:431093
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    References listed on IDEAS

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    1. Khaled Assi & Syed Masiur Rahman & Umer Mansoor & Nedal Ratrout, 2020. "Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol," IJERPH, MDPI, vol. 17(15), pages 1-17, July.
    2. Fang Zong & Hongguo Xu & Huiyong Zhang, 2013. "Prediction for Traffic Accident Severity: Comparing the Bayesian Network and Regression Models," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, October.
    3. Chen Zhang & Jie He & Yinhai Wang & Xintong Yan & Changjian Zhang & Yikai Chen & Ziyang Liu & Bojian Zhou, 2020. "A Crash Severity Prediction Method Based on Improved Neural Network and Factor Analysis," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-13, June.
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    Cited by:

    1. Abdulla Almahdi & Rabia Emhamed Al Mamlook & Nishantha Bandara & Ali Saeed Almuflih & Ahmad Nasayreh & Hasan Gharaibeh & Fahad Alasim & Abeer Aljohani & Arshad Jamal, 2023. "Boosting Ensemble Learning for Freeway Crash Classification under Varying Traffic Conditions: A Hyperparameter Optimization Approach," Sustainability, MDPI, vol. 15(22), pages 1-30, November.
    2. Stella Roussou & Thodoris Garefalakis & Eva Michelaraki & Tom Brijs & George Yannis, 2024. "Machine Learning Insights on Driving Behaviour Dynamics among Germany, Belgium, and UK Drivers," Sustainability, MDPI, vol. 16(2), pages 1-23, January.

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