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
[1] Peter T, Savolainen P T, Fred L, et al. The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives[J]. Accident Analysis and Prevention, 2011, 43(5):1666-1676.
[2] Chang H, Yeh T. Risk factors to driver fatalities in single-vehicle crashes: comparisons between non-motorcycle drivers and motorcyclists[J]. Journal of Transportation Engineering, 2006, 132 (3): 227-236.
[3] Malyshkina N, Mannering F. Empirical assessment of the impact of highway design exceptions on the frequency and severity of vehicle accidents[J]. Accident Analysis and Prevention, 2010, 42(1): 131-139.
[4] Yamamoto T, Shankar V. Bivariate ordered-response probit model of driver's and passenger's injury severities in collisions with fixed objects[J]. Accident Analysis and Prevention, 2004, 36(5): 869-876.
[5] Helai H, Chor C, Haque M. Severity of driver injury and vehicle damage in traffic crashes at intersections: a Bayesian hierarchical analysis[J]. Accident Analysis and Prevention, 2008, 40(1): 45-54.
[6] Lee J, Mannering F. Impact of roadside features on the frequency and severity of run-off-roadway accidents: an empirical analysis[J]. Accident Analysis and Prevention, 2002, 34(2): 149-161.
[7] Eluru N, Bhat C, Hensher D. A mixed generalized ordered response model for examining pedestrian and bicyclist injury severity level in traffic crashes[J]. Accident Analysis and Prevention, 2008, 40(3):1033-1054.
[8] 李世民,孙明玲,关宏志. 基于累积Logistic模型的交通事故严重程度预测模型[J].交通标准化,2009(3):168-171.Li Shi-min, Sun Ming-ling, Guan Hong-zhi. Prediction model cumulative logistic for severity of road traffic accident[J]. Transport Standardization, 2009(3):168-171.
[9] 马壮林,邵春福,李霞. 基于Logistic模型的公路隧道严重事故严重程度的影响因素[J].吉林大学学报:工学版,2010,40(2):423-426.Ma Zhuang-lin, Shao Chun-fu, Li Xia. Analysis of factors affecting accident severity in highway tunnels based on logistic model[J]. Journal of Jilin University(Engineering and Technology Edition),2010,40(2):423-426.
[10] Ma Zhuang-lin, Shao Chun-fu, Ma She-qiang, et al. Constructing road safety performance indicators using fuzzy delphi method and grey delphi method[J]. Expert Systems with Applications,2011,38(3):1509-1514.
[11] 侯树展,孙小瑞,贺玉龙,等.高速公路交通事故严重程度与交通流特征的关系研究[J].中国安全科学学报,2011(9):106-111.Hou Shu-zhan, Sun Xiao-rui, He Yu-long, et al. Relationships between crash severity and traffic flow characteristics on freeways[J]. China Safety Science Journal,2011(9):106-111.
[12] Mussone L, Ferrari A, Oneta M. An analysis of urban collisions using an artificial intelligence model[J]. Accident Analysis & Prevention, 1999, 31(6): 705-718.
[13] Chang L Y, Wang H W. Analysis of traffic injury severity: an application of non-parametric classification tree techniques[J]. Accident Analysis & Prevention, 2006, 38(5): 1019-1027.
[14] de Ona J, Mujalli R O, Calvo F J. Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks[J]. Accident Analysis and Prevention, 2011, 43 (1): 402-411.
[15] Delen D, Sharda R, Bessonov M. Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks[J]. Accident Analysis & Prevention, 2006, 38 (3): 434-444.
[16] Xie Yuan-chang , Lord Dominique , Zhang Yun-long. Predicting motor vehicle collisions using bayesian neural network models: an empirical analysis[J]. Accident Analysis & Prevention, 2011, 43(1): 402-411.
[17] de Oa Juan, Mujalli Randa Oqab, Calvo Francisco J. Analysis of traffic accident injury severity on spanish rural highways using bayesian networks[J]. Accident Analysis & Prevention, 2007, 39(5): 922-933.
[18] Li X G, Lord D, Zhang Y L, et al. Predicting motor-vehicle crashes using support vector machine models[J]. Accident Analysis & Prevention, 2008, 40(4): 1611-1618.
[19] 田英杰.支持向量回归机及其应用研究[D].北京:中国农业大学经济管理学院,2005.Tian Ying-jie. Support vector regression and its application[D]. Beijing:College of Economics & Management,China Agricultural University, 2005.
[20] 奉国和. SVM分类核函数及参数选择比较[J].计算机工程与应用,2011,47(3):123-128. Feng Guo-he. Parameter optimizing for support vector machines classification[J]. Computer Engineering and Applications,2011,47(3): 123-128.
[21] 董国君,哈力木拉提·买买提,余辉. 基于RBF核的SVM核参数优化算法[J]. 新疆大学学报:自然科学版,2009(3):355-358.Dong Guo-jun, Halmurat Maimait, Yu Hui. Algorithms of optimizing SVM's kernel parameters with RBF kernel[J]. Journal of Xinjiang University(Natural Science Edition),2009(3):355-358.
[22] 徐崇刚,胡远满,常禹,等. 生态模型的灵敏度分析[J]. 应用生态学报,2004,15(6):1056-1062.Xu Chong-gang,Hu Yuan-man,Chang Yu,et al.Sensitivity analysis of ecological modeling[J].Chinese Journal of Applied Ecology,2004,15(6):1056-1062.
[23] Li Zhi-bin, Liu Pan, Wang Wei, et al. Using support vector machine models for crash injury severity analysis[J]. Accident Analysis & Prevention, 2012(45): 478-486.
[24] GA/T859-2010.中华人民共和国公安部. 道路交通事故处理信息数据结构[S].
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