Machine Learning for Transportation Research and Applications
Published on 19. April 2023
252 pages
978-0-323-99680-8 (ISBN)
Description
Transportation is a combination of systems that presents a variety of challenges often too intricate to be addressed by conventional parametric methods. Increasing data availability and recent advancements in machine learning provide new methods to tackle challenging transportation problems. This textbookis designed for college or graduate-level students in transportation or closely related fields to study and understand fundamentals in machine learning. Readers will learn how to develop and apply various types of machine learning models to transportation-related problems. Example applications include traffic sensing, data-quality control, traffic prediction, transportation asset management, traffic-system control and operations, and traffic-safety analysis.
- Introduces fundamental machine learning theories and methodologies
- Presents state-of-the-art machine learning methodologies and their incorporation into transportationdomain knowledge
- Includes case studies or examples in each chapter that illustrate the application of methodologies andtechniques for solving transportation problems
- Provides practice questions following each chapter to enhance understanding and learning
- Includes class projects to practice coding and the use of the methods
More details
Other editions
Additional editions
Yinhai Wang | Zhiyong Cui | Ruimin Ke
Machine Learning for Transportation Research and Applications
Book
04/2023
Elsevier
€120.50
Shipment within 15-20 days
Persons
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 ComputerScience 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, machinelearning, computer vision, and edge computing.
Content
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 Modeling8. Probabilistic Models 9. Reinforcement Learning 10. Generative Models 11. Meta/Transfer Learning
Part Three: Future Research and Applications The Future of Transportation and AI
