交通事故严重程度C5.0决策树预测模型
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摘要
根据中国现行交通事故严重程度分类与事故信息数据分布特征,基于C5.0决策树方法,选取某省会城市城区及周边重点公路16 009起交通事故现场数据,分别将事故严重程度输出变量按照2分类和3分类,输入变量按照空间属性、涉事驾驶人及车辆属性和全属性,建立事故严重程度预测模型,生成相应规则集并利用测试样本进行检验和模型对比。研究结果表明:2分类和3分类事故严重程度预测模型精度分别为70%和61%,多模型综合优度有所提升;实证规则集揭示了影响事故严重程度分类的因素主要有,碰撞类型、道路属性、事故致因和驾驶人类型等。
Based on the algorithm of C5.0decision tree,current severity classification of traffic accidents and the distribution characteristics of accident information data,this paper used the field data of 16,009 traffic accidents which occurred in some main highways of the urban area in and around a certain capital city to analyze the accident severity,and established a prediction model of accident severity according to the output variables based on dichotomy and trichotomy as well as the input variables based on the spatial attributes,the driver involved,vehicle attributes and the overall attributes.Through the test,this paper got the appropriate rule set and used the test samples for inspection and the comparison of models.The results show that the accuracy of the prediction model in accident severity is 70%and 61%separately based on dichotomy and trichotomy,and the integrated goodness of multi-model is improved.The empirical rule set reveals that the factors influencing accident severity classification are mainly the type of collision,road attributes,accident causation and the type of driver.5tabs,2figs,14 refs.
Based on the algorithm of C5.0decision tree,current severity classification of traffic accidents and the distribution characteristics of accident information data,this paper used the field data of 16,009 traffic accidents which occurred in some main highways of the urban area in and around a certain capital city to analyze the accident severity,and established a prediction model of accident severity according to the output variables based on dichotomy and trichotomy as well as the input variables based on the spatial attributes,the driver involved,vehicle attributes and the overall attributes.Through the test,this paper got the appropriate rule set and used the test samples for inspection and the comparison of models.The results show that the accuracy of the prediction model in accident severity is 70%and 61%separately based on dichotomy and trichotomy,and the integrated goodness of multi-model is improved.The empirical rule set reveals that the factors influencing accident severity classification are mainly the type of collision,road attributes,accident causation and the type of driver.5tabs,2figs,14 refs.
引文
[1]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.
[2]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.
[3]Yamamoto T,Shankar V,Bivariate ordered-response probit model of driver’s and passenger’s injury severities in collisions with fixed objects[J].Accident Anal-ysis and Prevention,2004,36(5):869-876.
[4]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.
[5]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.
[6]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.
[7]Malyshkina N,Mannering F.Markov switching multinomial logit model:an application to accident-injury severities[J].Accident Analysis and Prevention,2009,41(4):829-838.
[8]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 and Prevention,2006,38(3):434-444.
[9]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-ming,SUN Ming-ling,GUAN Hong-zhi.Prediction model cumulative logistic for severity of road traffic accident[J].Transport Standardization,2009(3):168-171.(in Chinese)
[10]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.(in Chinese)
[11]Pei Y L,Fu C Y.Investigating crash Injury severity at unsignalized intersections in hei longjiang province,china[J].Journal of Traffic and Transportation Engineering:English Edition,2014,1(4):272-279.
[12]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.(in Chinese)
[13]GA/T 859-2010,道路交通事故处理信息数据结构[S].GA/T 859-2010,Data structure for accident information[S].
[14]GA/T 16.1-11,道路交通事故信息代码[S].GA/T 16.1-11,Codes for road traffic accident scene[S].
[2]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.
[3]Yamamoto T,Shankar V,Bivariate ordered-response probit model of driver’s and passenger’s injury severities in collisions with fixed objects[J].Accident Anal-ysis and Prevention,2004,36(5):869-876.
[4]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.
[5]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.
[6]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.
[7]Malyshkina N,Mannering F.Markov switching multinomial logit model:an application to accident-injury severities[J].Accident Analysis and Prevention,2009,41(4):829-838.
[8]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 and Prevention,2006,38(3):434-444.
[9]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-ming,SUN Ming-ling,GUAN Hong-zhi.Prediction model cumulative logistic for severity of road traffic accident[J].Transport Standardization,2009(3):168-171.(in Chinese)
[10]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.(in Chinese)
[11]Pei Y L,Fu C Y.Investigating crash Injury severity at unsignalized intersections in hei longjiang province,china[J].Journal of Traffic and Transportation Engineering:English Edition,2014,1(4):272-279.
[12]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.(in Chinese)
[13]GA/T 859-2010,道路交通事故处理信息数据结构[S].GA/T 859-2010,Data structure for accident information[S].
[14]GA/T 16.1-11,道路交通事故信息代码[S].GA/T 16.1-11,Codes for road traffic accident scene[S].