全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于SVM灵敏度的城市交通事故严重程度影响因素分析

, PP. 1315-1320

Keywords: 交通工程,事故严重程度,分类识别,支持向量机,智能算法

Full-Text   Cite this paper   Add to My Lib

Abstract:

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。

References

[1]  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.
[2]  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.
[3]  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.
[4]  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.
[5]  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.
[6]  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.
[7]  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.
[8]  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.
[9]  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.
[10]  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.
[11]  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.
[12]  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.
[13]  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.
[14]  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.
[15]  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].
[16]  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.
[17]  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.
[18]  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.
[19]  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.
[20]  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.
[21]  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.
[22]  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.
[23]  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.
[24]  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.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133