Investigating the Risk Factors on Crash Severity for Selected Risky Roads in Al-Diwaniyah City by Utilizing a Binary Probit Model

Abstract

Traffic crashes are one of the main reasons for the deaths of many people and the loss of property. Road safety is a crucial aspect of transportation that aims to prevent crashes and injuries on the road, and several contributing factors affect it. In this study, the binary probit model using the N-Logit software was applied to crash-related data to examine the contribution of several variables to severe crash outcomes in Al-Diwaniyah City. Crash severity (the dependent variable) in this study is a dichotomous variable with two categories, severe and non-severe. Because of the binary nature of this dependent variable, a binary probit model was found suitable. Out of 37 independent variables obtained from Al-Diwaniyah Hospital traffic crash reports between 2014 and 2021 and from fieldwork to evaluate the pavement surface condition using the pavement condition index (PCI), six were statistically significantly associated with severe crashes. These factors include driver age, spring and summer seasons, pavement surface conditions, pedestrian collisions, and multi-vehicle crashes. Some proposals were also recommended to reduce traffic crashes, such as using median barriers to regulate pedestrian crossing, managing a proper number of lanes in the roads to avoid congestion due to a large number of vehicles, assessing the condition of the pavement surface annually at least, to identify pavement defects and conduct appropriate maintenance. Therefore, governments and transportation agencies must prioritize regular pavement condition evaluation as part of their maintenance programs for roads and highways. In conclusion, road safety is a complex issue that requires a multi-faceted approach involving various stakeholders such as government agencies, law enforcement agencies, vehicle manufacturers, drivers, pedestrians, and other road users.