Machine Learning for Transportation Research and Applications - Image 1

£83.99

Elsevier Machine Learning for Transportation Research and Applications

Name: Machine Learning for Transportation Research and Applications
Brand: Elsevier
Price: 83.99 GBP
Availability: InStock

Economics

About as cheap as it gets. The only time it was cheaper was 3 months ago.

£84 today · all-time low £83 (Apr 2026) · usually £84

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Description

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. 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

Product Specifications

Brand: Elsevier
Format: paperback
ASIN: 0323961266
Category: Books > Subjects > Business, Finance & Law > Economics
Domain: Amazon UK
Release Date: 12 May 2023
Listed Since: 20 May 2022

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Elsevier Machine Learning for Transportation Research and Applications

About as cheap as it gets. The only time it was cheaper was 3 months ago.

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