Date Approved
9-24-2024
Embargo Period
9-24-2026
Document Type
Dissertation
Degree Name
Doctor of Philosophy (Ph.D.)
Department
Civil and Environmental Engineering
College
Henry M. Rowan College of Engineering
Advisor
Mohammad Jalayer, Ph.D.
Committee Member 1
Yusuf Mehta, Ph.D., P.E.
Committee Member 2
Nidhal Bouaynaya, Ph.D.
Committee Member 3
Anthony Breitzman, Ph.D.
Committee Member 4
Thomas Brennan, Ph.D., P.E.
Keywords
Crash;Distracted Driving;Hotspot;Human Factors;Observational Data;Texting
Subject(s)
Traffic accidents--Prevention
Disciplines
Civil Engineering | Transportation Engineering
Abstract
Distracted driving is a hazardous behavior responsible for 25% of all fatal traffic crashes in New Jersey. While traditional methods such as surveys, videos, and simulations have been used to identify and evaluate distracted driving events, these approaches primarily provide cross-sectional data that fails to capture the true rate of distraction on the road. This study addresses this limitation by collecting longitudinal data on distracted driving events across selected corridors in New Jersey. A data collection team continuously drove through these corridors, manually counting and video recording driver distraction events. The collected data was then analyzed to assess the significance of various temporal features and geometric roadway features on the distraction rate. Additionally, video data from the observational study was used to detect driving behaviors using a deep learning algorithm. The study further examined drivers' involvement in distraction in conjunction with other risky behaviors, such as speeding and seatbelt non-compliance. An incident-crash equivalence analysis was performed to identify distraction hotspots. The effects of vehicle type, driving lane, adverse weather conditions, and high visibility enforcement campaigns on distraction were analyzed. The findings of this study revealed that cellphone use is the most prevalent form of distraction. Distractions like receiving calls, grooming, and talking to passengers were significantly influenced by the time of day and roadway type. The results suggest that law enforcement and awareness campaigns should focus on roads with positive medians, wider shoulders, and signalized intersections. Additionally, the study recommends targeted countermeasures for specific conditions such as speeding, fast lane driving, adverse weather, and seatbelt non-compliance to effectively reduce distracted driving in New Jersey.
Recommended Citation
Hasan, Ahmed Sajid, "Distracted Driving: Identification, Contributing Factors, and Safety Countermeasures" (2024). Theses and Dissertations. 3297.
https://rdw.rowan.edu/etd/3297
