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

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基于SVM灵敏度的城市交通事故严重程度影响因素分析

孙轶轩1, 邵春福1, 岳昊1, 朱亮2   

  1. 1.北京交通大学 城市交通复杂系统理论与技术教育部重点实验室,北京 100044;
    2.中国铁道科学研究院运输及经济研究所,北京 100081
  • 收稿日期:2013-11-22 出版日期:2014-09-01 发布日期:2014-09-01
  • 通讯作者: 邵春福(1957),男,教授.研究方向:交通安全.E-mail:cfshao@ bjtu.edu.cn
  • 作者简介:孙轶轩(1982), 男, 博士研究生.研究方向:交通安全.E-mail:squallsyx@163.com
  • 基金资助:
    “973”国家重点基础研究发展计划项目(2012CB725403); 国家自然科学基金国际合作重大项目(71210001).

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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摘要： 基于某中小城市4881起交通事故现场数据,构建了基于“道路交通事故信息系统”事故数据的特征变量集;以一般事故、严重事故作为二分类标签,建立事故严重程度支持向量机(SVM)分类识别模型,并分别通过网格搜索法、遗传算法进行模型核参数寻优;最后,通过单因素局部灵敏度分析方法,研究各个特征变量对模型测试集分类精度的影响,进一步确定事故严重程度的核心影响因素。结果表明:SVM模型在训练集和测试集上的分类精度均在80%左右,表现出良好的分类识别效果和泛化能力;事故属性、车辆属性中有8个特征变量,显著影响SVM模型的分类精度。

关键词: 交通工程, 事故严重程度, 分类识别, 支持向量机, 智能算法

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

中图分类号: 

  • U491
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