Transportation Big Data

Theory and Methods

Zhiyuan Liu, Ziyuan Gu, Pan Liu

• Publisher: Elsevier
• Year: 2024
• eBook ISBN: 9780443338922
• Edition: 0
• Topic: Tecnología e ingeniería
• Subtopic: Transporte y navegación

Transportation Big Data: Theory and Methods is centered on the big data theory and methods. Big data is now a key topic in transportation, simply because the volume of data has increased exponentially due to the growth in the amount of traffic (all modes) and detectors. This book provides a structured analysis of the commonly used methods for handling transportation big data; it is supported by a wealth of transportation engineering examples, together with codes. It offers a concise, yet comprehensive, description of the key techniques and important tools in transportation big data analysis.- Covers big data applications in transportation engineering in real-world scenarios- Shows how to select different machine learning algorithms for processing, analyzing, and modeling transportation data- Provides an overview of the fundamental concepts of machine learning and how classical algorithms can be applied to transportation-related problems- Provides an overview of Python's basic syntax and commonly used modules, enabling practical data analysis and modeling tasks using Python

Table of contents

• Front Cover
• Transportation Big Data
• Transportation Big Data
• Copyright
• Contents
• Preface
• 1 - Introduction
• 2 - Data analysis in Python
• 3 - Data preprocessing and exploratory data analysis
• 4 - Data visualization
• 5 - Machine learning basics
• 6 - Linear models
• 7 - Support vector machine
• 8 - Decision tree
• 9 - Clustering analysis
• 10 - Ensemble learning
• 11 - Artificial neural networks
• 12 - Deep learning
• Notation
• Index
• Back Cover