I am a Postdoctoral Researcher at the Smart & Safe Transportation (SST) Lab at the University of Central Florida (UCF) (Supervisor: Dr. Mohamed Abdel-Aty). I received my doctoral degree in Civil Engineering from UCF in 2026 and my master’s and bachelor’s degrees in Transportation Engineering from Tongji University in 2023 and 2020, respectively (see my CV here).

My research focuses on data-driven transportation safety and operation modeling, with a particular emphasis on large-scale connected vehicle (CV) trajectory data applications. Specifically, my work encompasses driving-behavior-enhanced traffic safety modeling, CV-data-based network traffic operation and prediction, and innovative crash analytic methods (see my research here).

As of 2025, I have published 18 SCI papers in leading transportation journals, including Accident Analysis & Prevention, Analytic Methods in Accident Research, Transportation Research Part C, and IEEE Transactions on Intelligent Transportation Systems, with 5 as first author and 4 as corresponding author (see these publications here).

I also serve as a reviewer for more than 10 transportation journals, including Accident Analysis & Prevention, Transportation Research Part A/C/E, Expert Systems with Applications, and major conferences such as the Transportation Research Board Annual Meeting and World Transport Convention.

⭐ Featured Publications

Intersection Crash Analysis Considering Longitudinal and Lateral Risky Driving Behavior from Connected Vehicle Data: A Spatial Machine Learning Approach

Accident Analysis & Prevention (2025)

Lei Han*, Mohamed Abdel-Aty

This study proposes a spatial-ML framework integrating longitudinal and lateral risky driving behaviors from connected vehicle data for intersection crash analysis.

Crash Frequency Risky Driving Behavior Spatial Modeling CV Data ML

Transformer-Based Modeling of Abnormal Driving Events for Freeway Crash Risk Evaluation

Transportation Research Part C: Emerging Technologies (2024)

Lei Han, Rongjie Yu*, Chenzhu Wang, Mohamed Abdel-Aty

This study proposes a Transformer-based model for freeway crash risk evaluation using abnormal driving events, achieving 84.1% accuracy and 77.7% AUC and outperforming traditional methods. It also reveals the temporal-spatial decay and collective superposition effect of abnormal driving events.

Crash Prediction Risky Driving Behavior CV Data DL