摘要
为探究影响安徽省农村公路单车事故严重度的主要因素,利用因子分析法,将自变量转化为相互独立的公共因子,依据因子得分,利用K均值算法聚类事故数据;采用二元Logistic回归模型对各类别数据建立事故严重度模型。结果表明:相对于潜在类别分析,基于混合聚类结果构建的Logistic回归模型拟合优度、预测精度更优;性别、年龄、是否超速等仅在某一类别中显著;道路线形、地形等在多个类别中显著,但对于事故严重度的影响方向不同。
In order to explore key factors that affect severity of single vehicle crashes on rural highways in Anhui Province,factor analysis was employed to transform independent variables into independent common factors.Then,K-means algorithm was used to cluster crash data according to factor scores.Finally,a binary Logistic regression model for accident severity was developed for each cluster.The results indicate that compared with latent class analysis,Logistic regression model,based on hybrid clustering results,has better goodness-of-fit and higher prediction accuracy.Factors such as gender,age and overspeed are only significant in a certain cluster while road alignment and terrain are significant in many,but exert different influence directions on crash severity.
作者
杨慧敏
石琴
陈一锴
骆仁佳
YANG Huimin;SHI Qin;CHEN Yikai;LUO Renjia(School of Automotive and Transportation Engineering,Hefei University of Technology,Hefei Anhui 230009,China)
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2020年第8期129-136,共8页
China Safety Science Journal
基金
国家自然科学基金资助(71871078)。
