Machine Learning for Transportation Research and Applications

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. Introduces fundamental machine learning theories and methodologies Presents state-of-the-art machine learning methodologies and their integrations with transportation domain knowledge Includes case studies or examples in each chapter that illustrate the application of methodologies and techniques for solving transportation problems INDICE: 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

• ISBN: 978-0-323-96126-4
• Editorial: Elsevier
• Encuadernacion: Rústica
• Páginas: 275
• Fecha Publicación: 01/04/2023
• Nº Volúmenes: 1
• Idioma: Inglés