Citation

Hassanpour S, Afandizadeh S. Int. J. Transp. Eng. 2023; 11(2): 1401-1418.

Copyright

(Copyright © 2023, Tarahan Parseh Transportation Research Institute)

DOI

10.22119/ijte.2023.352078.1617

PMID

unavailable

Abstract

Road accidents reduce traffic safety due to injuries and fatalities. Investigating and prioritizing factors contributing to road accidents have been based on deficient traditional ways as they do not consider the probability density of factors contributing to road accidents. Accordingly, an examination of accident road factors based on the probability density seems necessary. Thus, this paper first aimed at using principle components analysis (PCA) as a statistical prioritization tool for identifying the main and sub-main factors that contribute to injury severity on Borujerd-Khorramabad as a four-lane rural highway during the years 2015 to 2017. Secondly, the multivariate Gaussian probability model was used as a probabilistic density approach to estimate the probability density based on the relationship between factors that contributing to injury severity and the Pearson correlation. The results obtained through PCA indicated that factors contributing to injury severity were ranked in terms of Eigen values and rotated component matrix.

FINDINGS from the PCA model showed that OS, PSL, AADT, SL, R, and S, as 6 important factors affecting the accident occurrence relevant to injury severity. The results of the probability density also showed that the relation between operating speed with posted speed limits, and the relation among segment length, operating speed and radius are considerable due to increasing the probability density of accident occurrence. Moreover, AADT with operating speed and operating speed with slope and radius have significant effects on the probability density of occurring accidents. The results of the present study show that applying a multivariate Gaussian probability model helps to estimate the probability density of the accident occurrence of factors contributing to road accidents based on their values.

Language: en