Doctoral Dissertations

Date of Award

12-2021

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Major

Civil Engineering

Major Professor

Asad J. Khattak

Committee Members

Asad J. Khattak, Candace Brakewood, Anahita Khojandi, Haileab Hilafu

Abstract

The performance of a transportation system highly depends on the drivers’ behavior because driving behavior is one of the main causes of traffic accidents and one of the key factors in fuel consumption and vehicle emissions studies. Specifically, the focus of this dissertation is on aggressive driving who not only put their selves and others at risk but also contribute more to greenhouse gas emissions. Recently, the development of advanced sensors, connected vehicles (CVs), location-based services (LBS) provided unprecedented access to new high-resolution microscopic-level data which can be used to evaluate and monitor instantaneous driving behavior and safety performance of different road types and facilities in a spatio-temporal domain. Therefore, the main objective of this study is to develop a framework to harness the new large-scale data to proactively detect aggressive drivers in different roadways and neighborhoods and investigate the impact of aggressive driving on crash frequency, crash severity, and fuel consumption and emissions. In this study, the concept of “driving volatility” which is a surrogate safety measure of unsafe and aggressive driving behavior is used. As a rigorous measure of micro-driving behavior, driving volatility shows the degree of deviation from the norm which can help us to predict driving behavior in the short term. This dissertation explores driving aggressiveness and transportation system performance at different levels: 1) Driver level; 2) Location level; 3) Roadway level; 4) Network level. In summary, the main contribution of this dissertation is to evaluate transportation system performance in correlation with driving behavior at different levels by harnessing big data from real-world driving conditions and integrating them with conventional data. This dissertation includes a variety of topics such as crash frequency/severity analysis, driving style classification, fuel consumption, and emission. The findings of this study provide new insight into the field of transportation at different levels. The methodology of this study can be used for monitoring driving behavior and detecting risky drivers on different roadways. This study also helps the practitioners to identify critical locations and facilities with high risky driving behaviors, fuel consumption, and emission for safety and fuel efficiency improvements.

Download
Files over 3MB may be slow to open. For best results, right-click and select "save as..."

Share

COinS