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Elsevier

Machine Learning for Transportation Research and Applications

  • 1 Edición - 19 de abril de 2023
  • Última edición
  • Autores: Yinhai Wang, Zhiyong Cui, Ruimin Ke
  • Idioma: Inglés

Transportation is a combination of systems that presents a variety of challenges often too intricate to be addressed by conventional parametric methods. Increasing data… Leer más

Descripción

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.

Puntos claves

  • 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

De interès para

Researchers and grad students in transportation and transportation engineering; Practitioners in transportation

Índice

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

Detalles del producto

  • Edición: 1
  • Última edición
  • Publicado: 19 de abril de 2023
  • Idioma: Inglés

Sobre los autores

YW

Yinhai Wang

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.
Afiliaciones y experiencia
Professor of Transportation Engineering and Founding Director of the Smart Transportation Applications and Research Laboratory, Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA.

ZC

Zhiyong Cui

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.
Afiliaciones y experiencia
Ph.D. Candidate in Civil Engineering (Intelligent Transportation Systems), University of Washington (UW), USA.

RK

Ruimin Ke

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.
Afiliaciones y experiencia
Assistant Professor, Department of Civil Engineering, University of Texas at El Paso,USA.

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