ISBN : 9780323961264
Author : Yinhai Wang,
Publisher : Elsevier
Year : 2023
Language : English
Type : Book
Description : Table of contents Cover image Title page Table of Contents Copyright About the authors Chapter 1: Introduction Abstract 1.1. Background 1.2. ML is promising for transportation research and applications 1.3. Book organization Bibliography Chapter 2: Transportation data and sensing Abstract 2.1. Data explosion 2.2. ITS data needs 2.3. Infrastructure-based data and sensing 2.4. Vehicle onboard data and sensing 2.5. Aerial sensing for ground transportation data 2.6. ITS data quality control and fusion 2.7. Transportation data and sensing challenges 2.8. Exercises Bibliography Chapter 3: Machine learning basics Abstract 3.1. Categories of machine learning 3.2. Supervised learning 3.3. Unsupervised learning 3.4. Key concepts in machine learning 3.5. Exercises Bibliography Chapter 4: Fully connected neural networks Abstract 4.1. Linear regression 4.2. Deep neural network fundamentals 4.3. Transportation applications 4.4. Exercises Bibliography Chapter 5: Convolution neural networks Abstract 5.1. Convolution neural network fundamentals 5.2. Case study: traffic video sensing 5.3. Case study: spatiotemporal traffic pattern learning 5.4. Case study: CNNs for data imputation 5.5. Exercises Bibliography Chapter 6: Recurrent neural networks Abstract 6.1. RNN fundamentals 6.2. RNN variants and related architectures 6.3. RNN as a building block for transportation applications 6.4. Exercises Bibliography Chapter 7: Reinforcement learning Abstract 7.1. Reinforcement learning setting 7.2. Value-based methods 7.3. Policy gradient methods for deep RL 7.4. Combining policy gradient and Q-learning 7.5. Case study 1: traffic signal control 7.6. Case study 2: car following control 7.7. Case study 3: bus bunching control 7.8. Exercises Bibliography Chapter 8: Transfer learning Abstract 8.1. What is transfer learning 8.2. Why transfer learning 8.3. Definition 8.4. Transfer learning steps 8.5. Transfer learning types 8.6. Case study: vehicle detection enhancement through transfer learning 8.7. Case study: parking information management and prediction system by attribute representation learning 8.8. Case study: transfer learning for nighttime traffic detection 8.9. Case study: simulation to real-world knowledge transfer for driving behavior recognition 8.10. Exercises Bibliography Chapter 9: Graph neural networks Abstract 9.1. Preliminaries 9.2. Graph neural networks 9.3. Case study 1: traffic graph convolutional network for traffic prediction 9.4. Case study 2: graph neural network for traffic forecasting with missing values 9.5. Case study 3: graph neural network (GNN) for vehicle keypoints' correction 9.6. Exercises Bibliography Chapter 10: Generative adversarial networks Abstract 10.1. Generative adversarial network (GAN) 10.2. Case studies: GAN-based roadway traffic state estimation 10.3. Case study: conditional GAN-based taxi hotspots prediction 10.4. Case study: GAN-based pavement image data transferring 10.5. Exercises Bibliography Chapter 11: Edge and parallel artificial intelligence Abstract 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 11.6. Case study 1: parallel and edge AI in multi-task traffic surveillance 11.7. Case study 2: edge AI in vehicle near-crash detection 11.8. Case study 3: federated learning for vehicle trajectory prediction 11.9. Exercises Bibliography Chapter 12: Future directions Abstract 12.1. Future trends of deep learning technologies for transportation 12.2. The future of transportation with AI 12.3. Book extension and future plan Bibliography Bibliography Index