Biography

Zhenyu Wang, Ph.D., is Senior Research Faculty and Principal Investigator with the ITS, Traffic Operations & Safety Program at the Center for Urban Transportation Research (CUTR), University of South Florida. With more than two decades of applied transportation research experience, his work integrates emerging technologies—including artificial intelligence, computer vision, machine learning, large-scale data analytics, and immersive VR/AR—with advanced statistical modeling and traffic engineering to address critical roadway safety and operational challenges. Dr. Wang has served as Principal Investigator or Co-PI on numerous research projects sponsored by the FHWA, FRA, FDOT, NCHRP, and U.S. DOT University Transportation Centers, addressing vulnerable road user safety, pavement friction, roadway lighting, highway-rail grade crossings, and connected and automated transportation.

A major focus of his research is translating methodological advances into practical decision-support software for transportation agencies, including the Detect-and-Track (DNT) video analytics tool, the Intelligent Lighting Analysis (LITA) system, and the Advanced Lighting Measurement System (ALMS). An active contributor to scholarly literature, he regularly publishes peer-reviewed articles and presents at the Transportation Research Board (TRB) Annual Meeting. Dr. Wang holds a Ph.D. in Transportation Engineering from the University of South Florida, an M.S. from Chang’an University, and a B.S. from Taiyuan University of Technology, and has extensive experience teaching graduate and undergraduate transportation courses.

Research Areas

Artificial Intelligence, Computer Vision, and Big Data Analytics
Transportation Safety and Safety Analytics
Advanced Statistical Modeling and Machine Learning
Pedestrian, Bicycle, and Motorcycle Safety
Pavement Friction and Infrastructure Safety
Intelligent Transportation Systems and TSM&O
Automated and Connected Transportation
Traffic Operations and Simulation
Roadway Lighting and Nighttime Safety
Highway-Rail Grade Crossing and Railway Safety
Naturalistic Driving Data Analytics
Virtual Reality and Augmented Reality for Transportation Training

Selected Research and Technology Development

Artificial Intelligence and Computer Vision

Application of AI, machine learning, computer vision, and large-scale transportation data to crash analysis, safety evaluation, traffic video analytics, automated incident detection, and transportation system management and operations.

Transportation Safety Analytics

Development and application of advanced statistical and machine-learning methods for crash-frequency, injury-severity, before-after, surrogate-safety, and risk analyses, with applications to pedestrians, bicyclists, motorcycles, roadway infrastructure, and traffic operations.

Pavement Friction and Safety

Safety analysis using continuous pavement friction measurement data, including statistical modeling of crash risk, development of safety performance relationships, and integration of pavement condition and safety information to support project prioritization.

Pedestrian and Vulnerable Road User Safety

Research involving pedestrian exposure, crash risk, automated video-based behavioral analysis, roadway lighting, signal operations, speed management, and realistic artificial pedestrian datasets for transportation safety applications.

Immersive VR/AR Training

Development and deployment of virtual and augmented reality simulations for immersive workforce training in roadway work-zone operations and railway safety.

Roadway Lighting Measurement and Nighttime Safety

High-resolution roadway lighting data collection, illuminance modeling, and crash risk analysis to enhance nighttime safety, supported by the ALMS measurement system and LITA AI/ML analysis tool.

Transportation Research Software

Development of practical research and decision-support systems including Detect-and-Track (DNT), Intelligent Lighting Analysis (LITA), Advanced Lighting Measurement System (ALMS), and other transportation data-analysis tools implemented in FDOT applications.

DNT (Detect-and-Track) LITA (Intelligent Lighting Analysis) ALMS (Advanced Lighting Measurement)

Education & Academic Credentials

Ph.D. in Transportation Engineering
University of South Florida, Tampa, FL
M.S. in Transportation Engineering
Chang’an University, Xi'an, China
B.S. in Electrical Engineering
Taiyuan University of Technology, Taiyuan, China