Machine Learning for Transportation Research and Applications,
Edition 1
By Yinhai Wang, Zhiyong Cui and Ruimin Ke

Publication Date: 25 Apr 2023
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.

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
About the author
By Yinhai Wang, 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, Ph.D. Candidate in Civil Engineering (Intelligent Transportation Systems), University of Washington (UW), USA. and Ruimin Ke, Assistant Professor, Department of Civil Engineering, University of Texas at El Paso,USA.
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

Book details
ISBN: 9780323961264
Page Count: 252
Retail Price : £95.95
Researchers and grad students in transportation and transportation engineering; Practitioners in transportation