Yinhai Wang, Zhiyong Cui, Ruimin Ke

Machine Learning for Transportation Research and Applications (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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 textbook
is 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 transportation domain knowledge
• Includes case studies or examples in each chapter that illustrate the application of methodologies and techniques 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

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 252
• Erscheinungstermin: 19. April 2023
• Englisch
• ISBN-13: 9780323996808
• Artikelnr.: 67812207

Autorenporträt

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.

Inhaltsangabe

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