Thursday, October 31, 2024 | 12:00 – 1:00 PM (ET)
Swift traffic incident detection is crucial for saving lives and property and for quickly responding to the incidents and restoring normal traffic conditions. This study addresses the gaps in traffic metrics-based detection methods, which often fail to promptly identify incidents, and CCTV video-based methods, which do not capture the impact range of incidents on upstream and downstream traffic. By utilizing drone thermal camera video and developing machine learning (ML) algorithms, this study innovatively segments live traffic monitoring videos into short clips using a sliding window approach, then vehicle trajectory images are generated from these clips, and a self-designed and trained ML algorithm determines whether these images indicate a traffic incident or non-recurrent congestion caused by an incident. This allows for the efficient detection of traffic incidents while accurately assessing their impact on upstream and downstream traffic conditions. To implement this research in practice, user-friendly Android and web-based interfaces were developed for real-time drone data transmission and visualization of detection results. Field tests confirmed that the system designed in this study can detect incidents 7 minutes earlier than traditional methods. Additionally, the system detected the upstream propagation of congestion caused by the incident. This not only validates the algorithm’s ability to promptly and accurately identify traffic incidents and assess their impact on traffic flow but also demonstrates the usability of the developed graphical user interface. In summary, the drone-based AI-powered real-time traffic incident detection system developed in this study provides an innovative approach to the efficient detection and management of traffic incidents.
About the Presenters:
Yu Yu Zhang, Ph.D., is a Professor with the Department of Civil and Environmental Engineering at the University of South Florida (USF). Dr. Zhang leads the Smart Urban Mobility Laboratory (SUM-Lab) at USF and serves as the Director for National Institute for Congestion Reduction (NICR), a USDOT National University Transportation Center. Dr. Zhang develops mathematical programming and solution algorithms, simulation tools, econometrics and statistical models, machine learning/deep learning methods for obtaining innovative solutions for more efficient, resilient, and sustainable multimodal transportation systems. Her recent efforts focus on challenging issues of emerging services and technologies in transportation, including Advanced Air Mobility and automated connected electrified and shared (ACES) Transportation.
Dr. Zhang is the recipient of 2020 Amazon Research Award, a prestigious research award supporting innovations at academic institutions and non-profit organizations worldwide and 2010 Fred Burggraf Award, which is conferred by the National Academies of Science Transportation Research Board for recognizing the excellence of young researchers. She has published more than 70 papers in top transportation journals such as Transportation Research Part B, Part C, Part D, and Part E.
Mr. Joseph Post is a Senior Research Associate in the Department of Civil & Environmental Engineering at the University of South Florida and Deputy Director of the Advanced Aerial Mobility program at the Center for Urban Transportation Research. He held various leadership positions at the Federal Aviation Administration (FAA), most recently as Acting Director of Systems Engineering. He has worked at the Congressional Budget Office, the Institute for Defense Analyses, TRW, The CNA Corporation, and Sikorsky Aircraft. Mr. Post is co-inventor of three patents relating to helicopter flight controls and holds FAA commercial and remote pilot certificates. He earned a B.S. in Aeronautics and Astronautics from M.I.T., an M.S. in Engineering and Applied Science from Yale University, and an M.A. in Economics from George Mason University.
Bai Li, Ph.D. is a researcher in Advanced Air Mobility at CUTR. He is a postdoctoral researcher with the Department of Civil and Environmental Engineering. He received his Ph.D. degree in Civil Engineering (Transportation) from Shanghai Jiao Tong University. His research interests focus on advanced air mobility/unmanned air mobility (AAM/UAM). In AAM research, he is currently involved in projects funded by the USDOT and FDOT. In addition, during his Ph.D. studies, he participated in several projects on applying unmanned aerial vehicles (UAVs) in transportation and environmental engineering funded by the Ministry of Science and Technology of China, the National Planning Office of Philosophy and Social Science of China, and the Shanghai Municipal Bureau of Ecology and Environment. Specifically, his research focuses on integrated network design and demand estimation, environmental impact analysis of AAM, and simulation platform development for performance analysis of AAM systems, etc.
Achilleas Kourtellis, Ph.D., is a senior research associate and the assistant director of the Intelligent Transportation Systems, Traffic Operations, and Safety program at the Center for Urban Transportation Research (CUTR) at the University of South Florida. He has been with CUTR for more than 10 years and worked on several traffic safety projects and for different sponsors including USDOT, FDOT, FHWA, FTA, NHTSA, NICR, and NCTR. He has conducted studies in evaluating safety-related technologies with data collection for rail transit bus, large trucks, passenger vehicles, motorcycles, pedestrians, and bicycles. He has also developed survey instruments and conducted research to improve traffic safety. He has served as principal and co-principal investigator on more than 30 projects and currently serves as the co-PI on the project presented in the webinar.
