摘要
为减少山区高速公路的交通事故发生率以及提升其交通安全性,对山区高速公路交通事故中涉及车辆数的影响因素进行分析。首先,对西部某高速近八年来发生的一万多起交通事故进行时空特性分析和起因分析;其次,以事故涉及车辆数为因变量,将其分为单车、双车、多车事故三个等级,并从驾驶员、车辆、行驶环境等层面选取8个潜在自变量作为ologit模型的分析因子,得到显著性小于0.05的6个自变量;最后,利用ologit模型对6个显著变量进行分类,通过分析得到各自变量分类优势比,并采用负二项回归模型验证自变量分类风险大小的正确性。结果表明:各自变量分类中的冬季、涉及大货、间距不足、收费站、晴、白天等分类优势比最大,且优势比在ologit模型分析时变化更大,说明ologit模型比负二项回归模型更适合用于分析事故涉及车辆数变量的分类优势比。结论表明,在交通安全治理时尽量控制优势比大的因素可减小事故车辆数,从而可间接降低严重事故的发生率,为高速公路管理提供有力的决策依据。
In order to reduce the incidence of traffic accidents and improve the traffic safety of mountainous expressway,the influencing factors of the number of vehicles involved in traffic accidents were analyzed.Firstly,the time-space characteristics and causes of more than 10000 traffic accidents in the past eight years of a certain expressway in Western China were analyzed.Secondly,taking the number of vehicles involved in the accident as the dependent variable,accidents were divided into three categories as single vehicle,double vehicle and multi vehicle accidents.Eight potential independent variables from aspects of driver,vehicle and driving environment were selected as the analysis factors of ologit model,and six independent variables with significance less than 0.05 were obtained.Finally,six significant variables were classified by ologit model,their classification odds ratio(OR)was obtained through analysis,and the correctness of the independent variable classification risk was verified by negative binomial regression(NB)model.The analysis results show that the advantage ratio such as winter,large cargo,insufficient spacing,toll station,sunny day and other categories is the largest,and the advantage ratio changes more in the analysis of the ologit model.It shows that the ordered logit model is more suitable than the negative binomial regression model to analyze the classification advantage ratio of the number of vehicles involved in accidents.The results show that controlling factors with large advantage ratio can reduce the number of accident vehicles,thus indirectly reduce the incidence of serious accidents,which provides a strong decision basis for highway management.
作者
陈波
姚红云
CHEN Bo;YAO Hong-yun(Transportation Institute,Chongqing Jiaotong University,Chongqing 400074,China)
出处
《科学技术与工程》
北大核心
2020年第34期14283-14288,共6页
Science Technology and Engineering
关键词
交通安全
ologit模型
事故涉及车辆数
负二项回归模型
优势比
traffic safety
ologit model
number of vehicles involved in accidents
negative binomial regression model
odds ratio
