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

Khaled Assi

doi:10.3390/ijerph17207598

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.

Highlights

road traffic crash (RTC) represent a critical public health problem worldwide
According to the WHO global status report on traffic safety, 1.35 million people die yearly as a result of traffic crashes, and traffic crash injuries are the main reason for deaths among young people
The accurate prediction of traffic crash severity contributes to generating crucial information, which can be used to adopt appropriate measures for reducing the aftermath of crashes

Summary

Introduction

Peden et al [1] stated that RTCs are a major cause of serious injuries and fatalities in many countries. According to the WHO global status report on traffic safety, 1.35 million people die yearly as a result of traffic crashes, and traffic crash injuries are the main reason for deaths among young people. It is reported that road crashes are the eighth major cause of death [2]. The accurate prediction of traffic crash severity contributes to generating crucial information, which can be used to adopt appropriate measures for reducing the aftermath of crashes. Accurate severity prediction of traffic crashes can help hospitals to provide medical care quickly if a crash occurs

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