Citation

Yang H, Shi Q, Chen Y, Luo R. China Saf. Sci. J. 2020; 30(8): 129-136.

Copyright

(Copyright © 2020, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2020.08.019

PMID

unavailable

Abstract

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. All rights reserved.

Language: zh