Transportation issues are often too complicated to be addressed by conventional parametric methods. Increasing data availability and recent advancements in machine learning provide new methods to tackle the challenging transportation problems. Readers will learn how to develop and apply different types of machine learning models to transportation related problems. Example applications include transportation data generations, traffic sensing, transportation mode recognition, transportation system management and control, traffic flow prediction, and traffic safety analysis.
1. Chapter 1 : Introduction - p. Page viii 2. Chapter 2 : Transportation data and sensing - p. Page 7 3. Chapter 3 : Machine learning basics - p. Page 24 4. Chapter 4 : Fully connected neural networks - p. Page 40 5. Chapter 5 : Convolution neural networks - p. Page 55 6. Chapter 6 : Recurrent neural networks - p. Page 79 7. Chapter 7 : Reinforcement learning - p. Page 93 8. Chapter 8 : Transfer learning - p. Page 115 9. Chapter 9 : Graph neural networks - p. Page 139 10. Chapter 10 : Generative adversarial networks - p. Page 164 11. Chapter 11 : Edge and parallel artificial intelligence - p. Page 182 12. Chapter 12 : Future directions - p. Page 212 13. Bibliography - p. Page 216 14. Index - p. Page 216
Transportation issues are often too complicated to be addressed by conventional parametric methods. Increasing data availability and recent advancements in machine learning provide new methods to tackle the challenging transportation problems. Readers will learn how to develop and apply different types of machine learning models to transportation related problems. Example applications include transportation data generations, traffic sensing, transportation mode recognition, transportation system management and control, traffic flow prediction, and traffic safety analysis.
1. Chapter 1 : Introduction - p. Page viii 2. Chapter 2 : Transportation data and sensing - p. Page 7 3. Chapter 3 : Machine learning basics - p. Page 24 4. Chapter 4 : Fully connected neural networks - p. Page 40 5. Chapter 5 : Convolution neural networks - p. Page 55 6. Chapter 6 : Recurrent neural networks - p. Page 79 7. Chapter 7 : Reinforcement learning - p. Page 93 8. Chapter 8 : Transfer learning - p. Page 115 9. Chapter 9 : Graph neural networks - p. Page 139 10. Chapter 10 : Generative adversarial networks - p. Page 164 11. Chapter 11 : Edge and parallel artificial intelligence - p. Page 182 12. Chapter 12 : Future directions - p. Page 212 13. Bibliography - p. Page 216 14. Index - p. Page 216
