Abstract

Speeding is a major concern leading to road accidents and fatalities. This research investigates speeding behavior and develops effective countermeasures to enhance road safety. It utilizes driving simulators, connected vehicle data, and dash cameras for analysis. The study focuses on urban roads in Central Florida, examining the effectiveness of speed management countermeasures like Pedestrian Hybrid Beacons (PHBs) and Rectangular Rapid Flashing Beacons (RRFBs). PHBs are more effective than RRFBs, reducing speeds by 40-50% compared to 30-36% reduction. Recommendations include educating drivers on PHBs and installing RRFBs on appropriate roads. The research uses connected vehicle data to analyze speeding behavior beyond specific locations. Machine learning models predict speeding proportions accurately by considering factors like travel time and residential areas. Driver trip factors and the environment significantly influence speeding. Spending more time stopped at signalized intersections increases the likelihood of high-speed driving, driven by the belief in saving time. Higher proportions of residential and commercial areas result in less speeding. These findings assist transportation planners in designing roads to reduce speeding. Image data analysis explores the impact of drivers' visual environment on speeding behavior. Elements within the visual surroundings, weather conditions, and driver-related variables affect speeding. A hurdle beta regression model considers driver heterogeneity and reveals significant correlations between speed, headway, and driving behavior in specific areas. Understanding these factors helps transportation engineers design safer roads, optimize road layouts, manage traffic flow, and implement speed management countermeasures. By understanding countermeasure effectiveness, transportation planners can ensure road safety. Considering speeding impacts beyond specific locations and drivers' visual environments improves roadway design. These findings contribute to a comprehensive understanding of speeding risks and aid road safety initiatives aligned with the Vision Zero approach of eliminating traffic-related fatalities and serious injuries.

Notes

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Graduation Date

2023

Semester

Summer

Advisor

Abdel-Aty, Mohamed

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Civil, Environmental, and Construction Engineering

Degree Program

Civil Engineering

Identifier

CFE0009809; DP0027917

URL

https://purls.library.ucf.edu/go/DP0027917

Language

English

Release Date

August 2024

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Campus-only Access)

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