Machine Learning for Transportation Research and Applications

Chapter 1: Introduction
Abstract
1.1. Background
1.2. ML is promising for transportation research and applications
Chapter 2: Transportation data and sensing
2.1. Data explosion
2.2. ITS data needs
2.3. Infrastructure-based data and sensing
2.4. Vehicle onboard data and sensing
Chapter 3: Machine Learning basics
3.1. Categories of machine learning
3.2. Supervised learning
3.3. Unsupervised learning
3.4. Key concepts in machine learning
3.5. Exercises
Chapter 4: Fully connected neural networks
4.1. Linear regression
4.2. Deep neural network fundamentals
Chapter 5: Convolution neural networks (CNNs)
Chapter 6: Recurrent neural networks (RNN)
Chapter 7: Reinforcement learning
Chapter 8: Transfer learning
Chapter 9: Graph neural networks (GNN)
Chapter 10: Generative adversarial networks (GANs)
Chapter 11: Edge and parallel Artificial Intelligence
11.1. Edge computing concept
11.2. Edge artificial intelligence
11.3. Parallel artificial intelligence
11.4. Federated learning concept
11.5. Federated learning methods
Bibliography
Chapter 12: Future directions
Bibliography
Index