Machine Learning for Transportation Research and Applications (eBook)

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.

• 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

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.

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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