Machine Learning for Transportation Research and Applications

Yinhai Wang - Ph.D., P.E., Professor, Transportation Engineering, University of Washington, USA. Dr. Yinhai Wang is a fellow of both the IEEE and American Society of Civil Engineers (ASCE). He also serves as director for Pacific Northwest Transportation Consortium (PacTrans), USDOT University Transportation Center for Federal Region 10, and the Northwestern Tribal Technical Assistance Program (NW TTAP) Center. He earned his Ph.D. in transportation engineering from the University of Tokyo (1998) and a Master in Computer Science from the UW (2002). Dr. Wang’s research interests include traffic sensing, transportation data science, artificial intelligence methods and applications, edge computing, traffic operations and simulation, smart urban mobility, transportation safety, among others. Zhiyong Cui - Ph.D., Associate Professor, School of Transportation Science and Engineering, Beihang University. Dr. Cui received the B.E. degree in software engineering from Beijing University in 2012, the M.S. degree in software engineering from Peking University in 2015, and the Ph.D. degree in civil engineering (transportation engineering) from the University of Washington in 2021. Dr. Cui’s primary research focuses on intelligent transportation systems, artificial intelligence, urban computing, and connected and autonomous vehicles. Ruimin Ke - Ph.D., Assistant Professor, Department of Civil Engineering, University of Texas at El Paso, USA. Dr. Ruimin Ke received the B.E. degree in automation from Tsinghua University in 2014, the M.S. and Ph.D. degrees in civil engineering (transportation) from the University of Washington in 2016 and 2020, respectively, and the M.S. degree in computer science from the University of Illinois Urbana–Champaign.Dr. Ke’s research interests include intelligent transportation systems, autonomous driving, machine learning, computer vision, and edge computing.

Part One: Overview 1. General Introduction and Overview 2. Fundamental Mathematics 3. Machine Learning Basics

Part Two: Methodologies and Applications 4. Classical ML Methods 5. Convolutional Neural Network 6. Graph Neural Network 7. Sequence Modeling 8. Probabilistic Models 9. Reinforcement Learning 10. Generative Models 11. Meta/Transfer Learning

Part Three: Future Research and Applications The Future of Transportation and AI