基于混合聚类的农村公路单车事故影响因素分析

作者：Yang Huimin; Shi Qin; Chen Yikai; Luo Renjia

来源：China Safety Science Journal, 2020, 30(8): 129-136.

DOI：10.16265/j.cnki.issn1003-3033.2020.08.019

• binary Logistic regression
• factor analysis
• K-means clustering
• latent class analysis
• rural highway
• single vehicle accident

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. ? 2020 China Safety Science Journal.

• 单位
  合肥工业大学