Assessing the Risk of University-Student Driving to enhance Safe and Sustainable Traffic Management: A Logistic Regression and Analytic Hierarchy Approach

Road accidents have become more common these days and it is pathetic when the accidents happen due to ignorance. The research objective is focused on university students, creating awareness about road safety, and improving a sustainable traffic management scenario in the Kingdom of Saudi Arabia. An analytical hierarchy process and a logistic regression model were used to determine the risk priorities ranking of severity factors based on the comparisons of different driver behavior factors. A cross-sectional survey was conducted among 3200 university students in Saudi Arabia to evaluate the risk associated with accident factors. The main factors taken for risk analysis were stability during driving, not adhering to rules, committing human errors, insufficient visibility, and facing vehicles issues. The model estimation analysis revealed the severity, which was based on the student's behavioral factors as a driver which contributed to the high fatality. It is further proposed to teach an interdisciplinary course on Traffic Management to various university students. The awareness of traffic sense and safety rules would bring down the accidents rates and help the government to maintain smooth traffic density.