Machine Learning for Transportation Research and Applications
- 1st Edition - April 19, 2023
- Latest edition
- Authors: Yinhai Wang, Zhiyong Cui, Ruimin Ke
- Language: English
Transportation is a combination of systems that presents a variety of challenges often too intricate to be addressed by conventional parametric methods. Increasing data… Read more
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Description
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 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.
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.
Key features
Key features
- 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
Readership
Readership
Researchers and grad students in transportation and transportation engineering; Practitioners in transportation
Table of contents
Table of contents
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
Product details
Product details
- Edition: 1
- Latest edition
- Published: April 19, 2023
- Language: English
About the authors
About the authors
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.
Affiliations and expertise
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.
Affiliations and expertise
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.
Affiliations and expertise
Assistant Professor, Department of Civil Engineering, University of Texas at El Paso,USA.View book on ScienceDirect
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