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Machine Learning for Transportation Research and Applications
Learn both fundamental and state-of-the-art machine learning (ML) methodologies, technologies, and applications for transportation research and applications
Yinhai Wang (Author), Zhiyong Cui (Author), Ruimin Ke (Author)
9780323961264, Elsevier Science
Paperback / softback, published 25 April 2023
252 pages
22.9 x 15.2 x 1.7 cm, 0.45 kg
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.
Part One: Overview 1. General Introduction and Overview 2. Fundamental Mathematics 3. Machine Learning Basics Part Two: Methodologies and Applications 4. Classical ML Methods 5. Convolutional Neural Network 6. Graph Neural Network 7. Sequence Modeling 8. Probabilistic Models 9. Reinforcement Learning 10. Generative Models 11. Meta/Transfer Learning Part Three: Future Research and Applications The Future of Transportation and AI
Subject Areas: The environment [RN], Environmental economics [KCN], The self, ego, identity, personality [JMS], Social, group or collective psychology [JMH]
