ORCID Identifier(s)

0000-0002-1240-3697

Graduation Semester and Year

2021

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Civil Engineering

Department

Civil Engineering

First Advisor

Stephen P Mattingly

Abstract

Driver distraction causes a major portion of motor vehicle crashes because distractions turn the driver’s attention away from driving the driving task, Intersections represent high risk environments because many conflict points within the intersection exist. At intersections with signalized traffic control, drivers may be more likely to become distracted than at other intersections. While distraction during the red indication may not seem to be a significant concern, this study investigates the role distraction plays in crashes at signalized intersections. Using the SHRP2 naturalistic driving study data, this study focuses on investigating the frequency and types of driver distraction, the causes of driver distraction, and factors affecting crashes and conflicts at signalized intersections. The statistical modeling and decision trees developed in this thesis indicate that many factors significantly influence distraction, but age or years of driving experience appears as a critical factor in the likelihood of distraction. Driver familiarity and mild congestion levels also appear to increase the probability of distraction. The reductions in distraction relate to factors that induce a greater focus on the driving task like weather, age (older adults), or vehicle position. Uncongested conditions appear to decrease distractions and the risk of a crash or near crash event. Distraction from an object inside the vehicle poses a significant crash or near crash risk at signalized intersections. Technology (cell phone) related distractions pose a safety risk even for drivers queued at a signalized intersection, and the high frequency of this distraction among all drivers makes it a significant concern.

Keywords

Distracted driving, Crash analysis, Logistic regression, Decision tree classifier, SHRP2 data

Disciplines

Civil and Environmental Engineering | Civil Engineering | Engineering

Comments

Degree granted by The University of Texas at Arlington

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