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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
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Request a sales quoteTransportation 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.
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.
- Introduces fundamental machine learning theories and methodologies
- Presents state-of-the-art machine learning methodologies and their incorporation into transportation
domain knowledge - Includes case studies or examples in each chapter that illustrate the application of methodologies and
techniques for solving transportation problems - Provides practice questions following each chapter to enhance understanding and learning
- Includes class projects to practice coding and the use of the methods
Researchers and grad students in transportation and transportation engineering; Practitioners in transportation
- 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
- No. of pages: 252
- Language: English
- Edition: 1
- Published: April 19, 2023
- Imprint: Elsevier
- Paperback ISBN: 9780323961264
- eBook ISBN: 9780323996808
YW
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.ZC
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.RK
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.Read Machine Learning for Transportation Research and Applications on ScienceDirect