Date of Award

12-2023

Degree Type

Thesis

Degree Name

Master of Science in Civil Engineering

Department

Civil and Environmental Engineering

First Advisor

Kidando, Emmanuel

Second Advisor

Jenkins, Jacqueline M.

Third Advisor

Kitali, Angela

Abstract

Motor vehicles have been an integral part of the American way of life, providing an unprecedented degree of mobility. Yet for all its advantages, motor vehicle crashes claim the lives of over 31,000 people in the United States every year, leaving more than three million injured. To prevent these crashes, the causes must be understood and addressed. This study developed a Bayesian Networks (BN) model – a model for reasoning “what-if” questions – to explore the relationship between drivers’ roadway familiarity, distracted driving, reckless driving, and crash severity at work zones. This study examined the crashes that occurred in Ohio between 2017 and 2022. The data used in this research was retrieved from the Ohio Department of Public Safety database. Findings from the BN revealed that familiar drivers were more likely to engage in distracted or reckless driving, especially in work zones at interchanges and intersections. The research shows that the probability of distracted driving crashes is twice as high for familiar drivers at interchanges compared to intersections. Furthermore, work zones with lane closures increase the likelihood of rear-end crashes. However, work zones with lane shifts or crossovers decrease the odds of rear-end crashes. It is important to note that rear-end crashes in interchange areas are more dangerous and result in more severe injuries than intersection work zone crashes. Male drivers are more likely to be involved in distraction-related crashes in intersections, whereas female drivers tend to be more distracted in interchange areas.

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