BIO

Web of Science Researcher ID:AAA-6539-2020;

ORCID id: https://orcid.org/0000-0001-6516-8144

Dr. Yina Wu is a Research Associate Professor in the Department of Civil, Environmental, and Construction Engineering at the University of Central Florida (UCF SST team). Dr. Wu received a Ph.D. Degree in Civil Engineering (Transportation) from the University of Central Florida. Her research interests include Traffic Safety Analysis, Connected and Autonomous Vehicle (CAV), Proactive Traffic Management, Traffic Simulation, Driving Simulator, Computer Vision Applications in Transportation Engineering. She is the PI or co-PI for 6 projects that are related to traffic safety and serves as a committee member for the TRB road weather committee. She has published 20 peer-reviewed papers related to real-time prediction, Connected and Autonomous Vehicles (CAVs), human/behavioral factors, computer vision technologies, and other aspects of traffic safety in top journals including Transportation Research Part C, Accident Analysis and Prevention, IEEE, Journal of Intelligent Transportation Systems, Transportation Research Record. One of her papers titled “Crash risk analysis during fog conditions using real-time traffic data” is one of the most cited papers in Accident Analysis and Prevention. Meanwhile, her contributions to improving computer vision based on road safety analysis and developing the UCF Automated Roadway Conflict Identification System (ARCIS) are significant. She presented the paper that introduced the ARCIS system during Transportation Research Board 99th Annual Meeting at Washington D.C., and the paper has been published at Transportation Research Record. She also has extensive experience in working with a multi-disciplinary team to develop transportation-orientated tools. She supported the team leader of the UCF SST team and involved the system development during the three stages of the USDOT visualization challenge. The team developed a system named " Real-time crash risk visualization tool for traffic safety management" to real-time predict, visualize, and prevent/mitigate traffic crashes, which won the USDOT Solve for Safety Visualization Challenge (team award) and the Prince Michael International Road Safety Award (team award).