China Safety Science Journal ›› 2020, Vol. 30 ›› Issue (8): 129-136.doi: 10.16265/j.cnki.issn1003-3033.2020.08.019

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Influencing factors for single vehicle accidents on rural highways based on hybrid clustering approach

YANG Huimin, SHI Qin, CHEN Yikai, LUO Renjia   

  1. School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei Anhui 230009, China
  • Received:2020-05-22 Revised:2020-07-13 Online:2020-08-28 Published:2021-07-15

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.

Key words: rural highway, single vehicle accident, latent class analysis, factor analysis, K-means clustering, binary Logistic regression

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