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

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Machine Learning for Transportation Research and Applications,

Edition 1

By Yinhai Wang, Zhiyong Cui and Ruimin Ke

Publication Date: 25 Apr 2023

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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.

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.

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

ISBN: 9780323961264

Page Count: 252

Retail Price : £95.95

Researchers and grad students in transportation and transportation engineering; Practitioners in transportation

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