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Machine Learning for Transportation Research and Applications

Wang Yinhai Cui, Zhiyong Ke, Ruimin
Heftet / 2023 / Engelsk

Produktdetaljer

ISBN
9780323961264
Publisert
2023-04-25
Utgiver
Elsevier - Health Sciences Division
Vekt
430 gr
Høyde
229 mm
Bredde
152 mm
Aldersnivå
U, 05
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
252

Forfatter
Wang Yinhai
Cui, Zhiyong
Ke, Ruimin

Biografisk notat

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. 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. 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.

Machine Learning for Transportation Research and Applications

Wang Yinhai Cui, Zhiyong Ke, Ruimin
Heftet / 2023 / Engelsk
Wang Yinhai Cui, Zhiyong Ke, Ruimin
Heftet / 2023 / Engelsk
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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 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.
Les mer
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
Les mer
Learn both fundamental and state-of-the-art machine learning (ML) methodologies, technologies, and applications　for transportation research and applications
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
Les mer

Relaterte produkter

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.
Les mer
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
Les mer
Learn both fundamental and state-of-the-art machine learning (ML) methodologies, technologies, and applications　for transportation research and applications
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
Les mer

Produktdetaljer

ISBN
9780323961264
Publisert
2023-04-25
Utgiver
Elsevier - Health Sciences Division
Vekt
430 gr
Høyde
229 mm
Bredde
152 mm
Aldersnivå
U, 05
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
252

Forfatter
Wang Yinhai
Cui, Zhiyong
Ke, Ruimin

Biografisk notat

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. 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. 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.

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