Machine Learning for Transportation Research and Applications

Wang, Yinhai/ Cui, Zhiyong/ Ke, Ruimin

Elsevier - Health Sciences Division（2023/04発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 252 p.
• 言語 ENG
• 商品コード 9780323961264
• DDC分類 388.0285631

Full Description

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.

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