Machine Learning for Transportation Research and Applications
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Transportation issues are often too complicated to be addressed by conventional parametric methods. Increasing data availability and recent advancements in machine learning provide new methods to tackle the challenging transportation problems. Readers will learn how to develop and apply different types of machine learning models to transportation related problems. Example applications include transportation data generations, traffic sensing, transportation mode recognition, transportation system management and control, traffic flow prediction, and traffic safety analysis.
Key Features
- Introduces fundamental machine learning theories and methodologies
- Presents state-of-the-art machine learning methodologies and their integrations with transportation domain knowledge
- Includes case studies or examples in each chapter that illustrate the application of methodologies and techniques for solving transportation problems
Readership
Researchers and grad students in transportation and transportation engineering; Practitioners in transportation
Table of Contents
Part One: Overview
1. General Introduction and Overview
2. Fundamental Mathematics
3. Machine Learning BasicsPart 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 LearningPart Three: Future Research and Applications
The Future of Transportation and AI
Product details
- No. of pages: 275
- Language: English
- Copyright: © Elsevier 2023
- Published: January 1, 2023
- Imprint: Elsevier
- Paperback ISBN: 9780323961264
About the Authors
Yinhai Wang
Dr. Yinhai Wang is a professor in transportation engineering and the founding director of the Smart Transportation Applications and Research Laboratory (STAR Lab) at the University of Washington (UW). He also serves as director for Pacific Northwest Transportation Consortium (PacTrans), USDOT University Transportation Center for Federal Region 10. He earned his Ph.D. in transportation engineering from the University of Tokyo (1998. Dr. Wang’s active research fields include traffic sensing, transportation data science, artificial intelligence (AI) methods and applications, edge computing, traffic operations and simulation, smart urban mobility, transportation safety, etc. Dr. Wang is chair of the AI and Advanced Computing Committee of the Transportation Research Board (TRB) and co-chair for the Connected and Autonomous Vehicle Impact Committee for American Society of Civil Engineers (ASCE). He served as president of the ASCE Transportation and Development Institute (T&DI) in 2018-2019. Dr. Wang received the IEEE International Smart Cities Conference’s Best Paper Award for 2020 and ASCE Journal of Transportation Engineering Best Paper Award for 2003. He was also the winner of Institute of Transportation Engineers (ITE) Innovation in Education Award for 2018.
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
Zhiyong Cui
Dr. Zhiyong Cui is a Ph.D. Candidate in Civil Engineering (Intelligent Transportation Systems) at the University of Washington (UW). He has been a research assistant in the Smart Transportation Applications and Research Laboratory (STAR Lab) since 2015. He received a M.S. degree in software engineering from Peking University in 2015. His primary research focuses on intelligent transportation systems, transportation artificial intelligence, urban computing, and connected and autonomous vehicles. Mr. Cui has co-authored 40 peer-reviewed publications in journals and conference proceedings. He also serves as the reviewer of 20 journals and conferences. Mr. Cui serves as a Member of the Transportation Research Board (TRB) standing committees on intelligent transportation systems (AHB15) and on geospatial data acquisition committee (AFB80) and an Associate Member of the ASCE T&DI Artificial Intelligence Committee.
Affiliations and Expertise
Ph.D. Candidate in Civil Engineering (Intelligent Transportation Systems), University of Washington (UW), USA
Ruimin Ke
Dr. Ruimin Ke is a research associate working at the Smart Transportation Applications and Research (STAR) Lab at the UW. He has a Ph.D. in Intelligent Transportation Systems from University of Washington (2020. Dr. Ke’s active research fields include intelligent transportation systems, transportation data science, intelligent vehicle, smart infrastructure, edge computing, and artificial intelligence in transportation. He was a research assistant at the University of Washington for six years. He has worked on a variety of research projects funded by Federal Transit Administration (FTA), Washington State Department of Transportation (WSDOT), US Department of Transportation (USDOT) Region 10 University Transportation Center (UTC), Norwegian Public Road Administration (NPRA), Transportation Research Board (TRB) IDEA Program, and Sound Transit. Dr. Ke’s academic accomplishment has been recognized with a few awards; for example, the 2018 ITE Outstanding Graduate Student Award, and the Grand Prize of the 2018 International Transportation Challenge. Dr. Ke also serves as a member of the TRB Standing Committee on Statewide/National Transportation Data and Information Systems (AED10) and a young member of the ASCE T&DI Infrastructure Systems Committee.
Affiliations and Expertise
Research Associate, Smart Transportation Applications and Research (STAR) Lab, University of Washington (UW), USA
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