Wow! eBook
Machine Learning for Transportation Research and Applications
Author(s): Wang (Author), Cui (Author), Ke (Author)
- Publisher: Elsevier
- Publication Date: 12 May 2023
- Edition: 1st
- Language: English
- Print length: 252 pages
- ISBN-10: 0323961266
- ISBN-13: 9780323961264
Book Description
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.
- 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
Editorial Reviews
Review
About the Author
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.
