吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (5): 1315-1320.doi: 10.7964/jdxbgxb201405015

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Urban traffic accident severity analysis based on sensitivity analysis of support vector machine

SUN Yi-xuan1,SHAO Chun-fu1,YUE Hao1,ZHU Liang2   

  1. 1.MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China;
    2.Transportation & Economic Research Institute, China Academy of Railway Sciences, Beijing 100081,China
  • Received:2013-11-22 Online:2014-09-01 Published:2014-09-01

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Abstract: According to 4881 crash scene investigation data of accident database of a middle-size city, a Support Vector Machine (SVM) model is established for accident severity recognition, which is classified into low risk (property loss only) and high risk (injury or death involved). Grid Search (GS) and Genetic Algorithm (GA) are applied to find the best combination of penalty parameter C and Radial Basis Function (RBF) kernel parameter g. Then, the sensitivity analysis method is employed to evaluate the potential impacts of variables on the accident severity. The results show that the accuracies of the SVM model on both training and testing datasets are around 80%, which means better generalization performance: 8 variables of accident and vehicle attributes significantly influence the accident severity classification, which can be inferred as key factors.

Key words: traffic engineering, traffic accident severity, classification recognition, support vector machine(SVM), intelligent algorithm

CLC Number: 

  • U491
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