Abstract

This research aims to develop a comprehensive crash prediction strategy incorporating long and short-term predictive models on freeways. The first component of the proposed approach is the safety performance function (SPF), a long-term model that predicts the number of crashes per year. The second component is real-time crash prediction, the short-term predictive model which predicts the near future crash occurrence. Although the objectives of the SPFs and real-time crash prediction models are slightly different, both methods could be complementary sources of each other. Thus, this study proposed real-time crash prediction models integrated with SPFs. First, this study developed SPFs considering active traffic management (ATM) systems and the types of segments on the freeway. This study proposed safety performance functions for freeways with high occupancy toll (HOT) lanes. Moreover, this study provided SPFs for weaving segments as the most unstable along the freeways. Next, real-time crash prediction models were developed using machine learning techniques. Traffic and weather information was projected to the time-space plane to generate the dataset with spatial-temporal relationships. Convolutional Neural Network (CNN), widely utilized for image classification, was adopted for crash prediction. This study focused on the rear-end, sideswipe, and angle crashes on freeway weaving segments. To incorporate the more general and historical safety conditions of each segment, the expected number of crashes per year for specific time periods was utilized as additional input. The result showed that the proposed model correctly predicts more than 80% of crashes with an acceptable false alarm rate. In conclusion, integrating safety performance functions and real-time models represents a promising approach to advancing crash prediction capabilities. By combining the strengths of both components, this integrated framework enables more accurate and timely predictions, contributing to developing effective strategies for preventing roadway crashes and improving overall transportation safety.

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

CFE0009776; DP0027884

URL

https://purls.library.ucf.edu/go/DP0027884

Language

English

Release Date

August 2026

Length of Campus-only Access

3 years

Access Status

Doctoral Dissertation (Campus-only Access)

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Restricted to the UCF community until August 2026; it will then be open access.

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