Volume 08
Investigating the Factors Affecting the Severity of Single-Vehicle Crashes on Urban Roads using Bayesian Binary Probit Regression
Authors
Mohammad Rahmaninezhad Asil*, Iraj Bargegol
Abstract
Traffic crashes are a global challenge, and many people lose their lives in crashes every year. Single-vehicle accidents have high injury and death rates and are therefore a major concern. In this study, the effect of various factors on the severity of this type of crash on urban roads of Rasht, in Iran, has been investigated. Rasht is the capital of Guilan province and is one of the cities that many tourists visit every year. Investigating Single-vehicle crashes and predicting the factors affecting them facilitates the possibility of prevention and provision of medical facilities. In this paper, a Bayesian Binary Probit Regression model was used for this purpose. The results of this study showed that Single-vehicle crashes during non-peak traffic hours, which are from 21 to 6 o’clock, increase the severity of the collision and also the results stated that spring and summer seasons, Iranian-made passenger cars and motorcycles, male drivers, and inability to control vehicles increase the severity of a single-vehicle crash.
Keyword: Crash Severity, Single-vehicle Crashes, Urban Roads, Bayesian Binary Probit Regression.
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