The crash injury severity prediction of traffic accident using an improved wrappers feature selection algorithm

Abstract

The crash injury severity prediction of traffic accident is the prerequisite for driving decisions under dangerous conditions. This paper puts forward a new wrapper feature selection algorithm to improve the prediction accuracy of the injury severity. Firstly, based on Gini index and Mutual information, the importance of factors is analyzed by the comprehensive evaluation criteria, according to the importance score, the factors were selected by sequential backward selection. Secondly, three models, that is RF (Random Forest), C4.5 (C4.5 Decision Tree) and SVM (Support Vector Machine) are established to verify the prediction accuracy of selected subsets. In order to verify the proposed algorithm, based on the FARS(Fatality Analysis Reporting System) in the NHTSA(National Highway Traffic Safety Administration), a total of 6,295 rear-end accidents including 23 candidate factors were screened, the results demonstrate that the accuracy of the three models increases with the elimination of redundant variables, among them the SVM model has better predictive performance, the significant factors are obtained which include seat belts, airbag, the relative speed, vehicle weight, etc. finally the sensitivity analysis of significant factors is implemented and some suggestions are given to improve traffic safety.

Keywords:

• Crash injury
• severity prediction；wrappers variable selection
• Gini index
• mutual information

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Natural Science Foundation of Shandong Province under Grant No. ZR2019MF056.