Abstract

This comprehensive dissertation constitutes a significant contribution to the ongoing global discourse on road safety. Through a judicious utilization of advanced data analysis techniques, with a particular emphasis on machine learning applications, this research endeavors to address and bridge crucial gaps in our comprehension of multifaceted aspects related to road safety. Specifically, the study aims to delve into the intricacies of accident severity factors, driver characteristics, vehicle attributes, and the complex dynamics of road conditions. By systematically exploring these dimensions, the research endeavors to unearth more nuanced and precise relationships that influence accident outcomes. Moreover, a particular focus is dedicated to unraveling the intricate interplay between driver demographics, such as age and gender, and their interactions with other pertinent variables. The dissertation also places a spotlight on the often-overlooked potential of advanced data analysis techniques, underscoring their capability to extract profound insights from extensive datasets pertaining to road accidents. As the research unfolds, due acknowledgment is given to the evolving landscape of vehicle technologies, and a thorough assessment is conducted to discern their impact on road safety. This nuanced analysis contributes significantly to the overarching goal of developing evidence-based safety measures and fostering informed policymaking. The ultimate aim is to mitigate the societal toll of road accidents and pave the way for a safer and more secure transportation ecosystem globally. The thesis is structured into six chapters: Introduction, Literature Review, Research Methodology, Findings and Data Analysis, Discussion, and Conclusions, each addressing specific aspects of the research process and outcomes.

Library of Congress Subject Headings

Traffic safety--Automation; Automobile drivers--Data processing; Automatic data collection systems; Behavioral assessment; Machine learning

Publication Date

4-22-2024

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Ehsan Warriach

Campus

RIT Dubai

Plan Codes

PROFST-MS

Download

Share

COinS