Machine Learning for Transportation Research and Applications

1st Edition - April 19, 2023
Authors: Yinhai Wang, Zhiyong Cui, Ruimin Ke
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 6 1 2 6 - 4
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 9 6 8 0 - 8

Transportation is a combination of systems that presents a variety of challenges often too intricate to be addressed by conventional parametric methods. Increasing data… Read more

Machine Learning for Transportation Research and Applications

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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.

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

Yinhai Wang

Yinhai Wang - Ph.D., P.E., Professor, Transportation Engineering, University of Washington, USA. Dr. Yinhai Wang is a fellow of both the IEEE and American Society of Civil Engineers (ASCE). He also serves as director for Pacific Northwest Transportation Consortium (PacTrans), USDOT University Transportation Center for Federal Region 10, and the Northwestern Tribal Technical Assistance Program (NW TTAP) Center. He earned his Ph.D. in transportation engineering from the University of Tokyo (1998) and a Master in Computer Science from the UW (2002). Dr. Wang’s research interests include traffic sensing, transportation data science, artificial intelligence methods and applications, edge computing, traffic operations and simulation, smart urban mobility, transportation safety, among others.

Affiliations and expertise

Professor of Transportation Engineering and Founding Director of the Smart Transportation Applications and Research Laboratory, Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA.

Zhiyong Cui

Zhiyong Cui - Ph.D., Associate Professor, School of Transportation Science and Engineering, Beihang University. Dr. Cui received the B.E. degree in software engineering from Beijing University in 2012, the M.S. degree in software engineering from Peking University in 2015, and the Ph.D. degree in civil engineering (transportation engineering) from the University of Washington in 2021. Dr. Cui’s primary research focuses on intelligent transportation systems, artificial intelligence, urban computing, and connected and autonomous vehicles.

Affiliations and expertise

Ph.D. Candidate in Civil Engineering (Intelligent Transportation Systems), University of Washington (UW), USA.

Ruimin Ke

Ruimin Ke - Ph.D., Assistant Professor, Department of Civil Engineering, University of Texas at El Paso, USA. Dr. Ruimin Ke received the B.E. degree in automation from Tsinghua University in 2014, the M.S. and Ph.D. degrees in civil engineering (transportation) from the University of Washington in 2016 and 2020, respectively, and the M.S. degree in computer science from the University of Illinois Urbana–Champaign.Dr. Ke’s research interests include intelligent transportation systems, autonomous driving, machine learning, computer vision, and edge computing.

Affiliations and expertise

Assistant Professor, Department of Civil Engineering, University of Texas at El Paso,USA.