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.

摘要

交通事故的碰撞伤害严重程度预测是危险条件下驾驶决策的前提。本文提出了一种新的包装特征选择算法，以提高损伤严重程度的预测精度。首先，基于基尼指数和互信息，通过综合评价标准分析因素的重要性，根据重要性得分，依次向后选择因素进行选择。其次，建立RF（随机森林）、C4.5（C4.5决策树）和SVM（支持向量机）三个模型来验证所选子集的预测精度。为了验证所提出的算法，基于NHTSA（美国国家公路交通安全管理局）中的FARS（Fatality Analysis Reporting System），共有6个，