Название: Machine Learning for Transportation Research and ApplicationsАвтор: Yinhai Wang, Zhiyong Cui, Ruimin Ke
Издательство: Elsevier
Год: 2023
Страниц: 254
Язык: английский
Формат: pdf (true), epub
Размер: 33.3 MB
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 (ML). 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. The Chapter 2 introduces a spectrum of key concepts in the field of Machine Learning, starting with the definition and categories of Machine Learning, and then covering the basic building blocks of advanced Machine Learning algorithms. The theory behind the common regressions, including linear regression and logistic regression, gradient descent algorithms, regularization, and other key concepts of Machine Learning are discussed.
Автор: Алексей Карпов, Эльмира Шабурова
Автор: Деннис Тейлор
Автор: Морис Леблан
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