Machine Learning for Transportation Research and Applications
eBook - ePub

Machine Learning for Transportation Research and Applications

  1. 252 pages
  2. English
  3. ePUB (mobile friendly)
  4. 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

Frequently asked questions

Yes, you can cancel anytime from the Subscription tab in your account settings on the Perlego website. Your subscription will stay active until the end of your current billing period. Learn how to cancel your subscription.
No, books cannot be downloaded as external files, such as PDFs, for use outside of Perlego. However, you can download books within the Perlego app for offline reading on mobile or tablet. Learn more here.
Perlego offers two plans: Essential and Complete
  • Essential is ideal for learners and professionals who enjoy exploring a wide range of subjects. Access the Essential Library with 800,000+ trusted titles and best-sellers across business, personal growth, and the humanities. Includes unlimited reading time and Standard Read Aloud voice.
  • Complete: Perfect for advanced learners and researchers needing full, unrestricted access. Unlock 1.4M+ books across hundreds of subjects, including academic and specialized titles. The Complete Plan also includes advanced features like Premium Read Aloud and Research Assistant.
Both plans are available with monthly, semester, or annual billing cycles.
We are an online textbook subscription service, where you can get access to an entire online library for less than the price of a single book per month. With over 1 million books across 1000+ topics, we’ve got you covered! Learn more here.
Look out for the read-aloud symbol on your next book to see if you can listen to it. The read-aloud tool reads text aloud for you, highlighting the text as it is being read. You can pause it, speed it up and slow it down. Learn more here.
Yes! You can use the Perlego app on both iOS or Android devices to read anytime, anywhere — even offline. Perfect for commutes or when you’re on the go.
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

Publisher
Elsevier
Year
2023
Print ISBN
9780323961264
eBook ISBN
9780323996808

Table of contents

  1. Cover
  2. Front Matter
  3. Table of Contents
  4. Copyright
  5. Contents
  6. About the authors
  7. List of Illustrations
  8. List of Tables
  9. Chapter 1 : Introduction
  10. Chapter 2 : Transportation data and sensing
  11. Chapter 3 : Machine learning basics
  12. Chapter 4 : Fully connected neural networks
  13. Chapter 5 : Convolution neural networks
  14. Chapter 6 : Recurrent neural networks
  15. Chapter 7 : Reinforcement learning
  16. Chapter 8 : Transfer learning
  17. Chapter 9 : Graph neural networks
  18. Chapter 10 : Generative adversarial networks
  19. Chapter 11 : Edge and parallel artificial intelligence
  20. Chapter 12 : Future directions
  21. Bibliography
  22. Index
  23. A