
eBook - ePub
Machine Learning for Transportation Research and Applications
- 252 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
eBook - ePub
Machine Learning for Transportation Research and Applications
About this book
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
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Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app.
Yes, you can access Machine Learning for Transportation Research and Applications by Yinhai Wang,Zhiyong Cui,Ruimin Ke in PDF and/or ePUB format, as well as other popular books in Psychology & Economic Theory. We have over one million books available in our catalogue for you to explore.
Information
Table of contents
- Cover
- Front Matter
- Table of Contents
- Copyright
- Contents
- About the authors
- List of Illustrations
- List of Tables
- Chapter 1 : Introduction
- Chapter 2 : Transportation data and sensing
- Chapter 3 : Machine learning basics
- Chapter 4 : Fully connected neural networks
- Chapter 5 : Convolution neural networks
- Chapter 6 : Recurrent neural networks
- Chapter 7 : Reinforcement learning
- Chapter 8 : Transfer learning
- Chapter 9 : Graph neural networks
- Chapter 10 : Generative adversarial networks
- Chapter 11 : Edge and parallel artificial intelligence
- Chapter 12 : Future directions
- Bibliography
- Index
- A