Announcing the Final Examination of MD RAKIBUL ISLAM for the degree of Doctor of Philosophy
Real-time crash prediction is a complex task, with no existing framework to predict crash likelihood, types, and severity together along with a real-time traffic management strategy. Developing such a framework presents various challenges, including not independent and identically distributed data, imbalanced data, large model size, high computational cost, missing data, sensitivity vs. false alarm rate (FAR) trade-offs, estimation of traffic restoration time after crash occurrence, and real-world deployment strategy. A novel spatial ensemble distillation learning modeling technique is proposed to address these challenges. First, large-scale real-time data was used to develop a crash likelihood prediction model. Second, the proposed crash likelihood model's viability in predicting specific crash types were tested for real-world application. Third, the framework was extended to predict crash severity in real-time, categorizing crashes into four levels. Results show strong performance with sensitivities of 90.35%, 94.80%, and 84.23% for all crashes, rear-end crashes, and sideswipe/angle crashes, and 83.32%, 81.25%, 83.08%, and 84.59% for fatal, severe, minor injury, and PDO crashes, respectively, with very low FARs. This methodology also reduces model size, lowers costs, improves sensitivity, and reduces FAR. These results will be used by traffic management center for taking measures to prevent crashes in real-time through active traffic management strategies. The framework was further extended for efficient traffic management after any crash occurrence despite adopting these strategies. Particularly, the framework was extended to predict the traffic state after a crash, predict the traffic restoration time based on the estimated post-crash traffic state and apply a three-step validation technique to evaluate the performance of the developed approach. A real-world project, based on this framework, is currently being deployed and the mechanism is discussed. Overall, the methodologies presented in this dissertation offer multifaceted novel contributions and have excellent potential to reduce fatalities and injuries.
Committee in Charge:
Mohamed Abdel—Aty, Chair, Department of Civil, Environmental and Construction Engineering
Samiul Hasan, Associate Professor, Department of Civil, Environmental and Construction Engineering, University of Central
Florida, USA
Zubayer Islam, Postdoctoral Scholar, Department of Civil, Environmental and Construction Engineering, University of
Central Florida, USA
Dongdong Wang, Postdoctoral Scholar, Department of Civil, Environmental and Construction Engineering, University of
Central Florida, USA
Xin Yan, Statistics and Data Science