Abstract
AbstractMultivariate analysis can be used to identify the effects of several factors on the causes of a crash compared with univariate analysis. This paper uses a multivariate-analysis technique, the multinomial logistic regression (MLR) model, to examine the differences in crash-contributing factors for six collision types for both divided- and undivided-highway nonjunctions, given that a crash has occurred. Multinomial logistic regression was used to investigate (1) single-vehicle and (2) multivehicle collisions, which included (1) angular, (2) head-on, (3) rear-end, (4) sideswipe-same-direction, and (5) sideswipe-opposite-direction collisions. The risks associated with different collision types were found to be significantly influenced by various vehicle actions. The risk of sideswipe-same-direction collisions was higher while changing lanes and merging on undivided and divided highways. Similarly, while merging, drivers were prone to angular collisions, and when slowing down to rear-end collisions on ...
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
