Book pages in publisher website:

Mobility Patterns, Big Data and Transport Analytics. Tools and Applications for Modeling

Description

Mobility Patterns, Big Data and Transport Analytics provides a guide to the new analytical framework and its relation to big data, focusing on capturing, predicting, visualizing and controlling mobility patterns - a key aspect of transportation modeling. The book features prominent international experts who provide overviews on new analytical frameworks, applications and concepts in mobility analysis and transportation systems. Users will find a detailed, mobility ‘structural’ analysis and a look at the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications and transportation systems analysis that are related to complex processes and phenomena.

This book bridges the gap between big data, data science, and transportation systems analysis with a study of big data’s impact on mobility and an introduction to the tools necessary to apply new techniques.

The book covers in detail, mobility ‘structural’ analysis (and its dynamics), the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications, and transportation systems analysis related to complex processes and phenomena. The book bridges the gap between big data, data science, and Transportation Systems Analysis with a study of big data’s impact on mobility, and an introduction to the tools necessary to apply new techniques.

Key Features

• Guides readers through the paradigm-shifting opportunities and challenges of handling Big Data in transportation modeling and analytics
• Covers current analytical innovations focused on capturing, predicting, visualizing, and controlling mobility patterns, while discussing future trends
• Delivers an introduction to transportation-related information advances, providing a benchmark reference by world-leading experts in the field
• Captures and manages mobility patterns, covering multiple purposes and alternative transport modes, in a multi-disciplinary approach
• Companion website features videos showing the analyses performed, as well as test codes and data-sets, allowing readers to recreate the presented analyses and apply the highlighted techniques to their own data

Readership

Transport researchers, practitioners, and consultants, Undergraduate and graduate students in transportation programs, Transport policy makers.

Table of Contents

Chapter 1 - Big Data and Transport Analytics: An Introduction

Part I: Methodological

Chapter 2 - Machine Learning Fundamentals

Chapter 3 - Using Semantic Signatures for Social Sensing in Urban Environments

Chapter 4 - Geographic Space as a Living Structure for Predicting Human Activities   Using Big Data

Chapter 5 - Data Preparation

Chapter 6 - Data Science and Data Visualization

Chapter 7 - Model-Based Machine Learning for Transportation

Case Study 1: Taxi Demand in New York City

Case Study 2: Travel Mode Choices

Case Study 3: Freeway Occupancy in San Francisco

Case Study 4: Incident Duration Prediction

Chapter 8 -Textual Data in Transportation Research: Techniques and Opportunities

Part II: Applications

Chapter 9 - Statewide Comparison of Origin-Destination Matrices Between California Travel Model and Twitter

Chapter 10 - Transit Data Analytics for Planning, Monitoring, Control, and Information

Chapter 11 - Data-Driven Traffic Simulation Models: Mobility Patterns Using Machine Learning Techniques

Chapter 12 - Big Data and Road Safety: A Comprehensive Review

Chapter 13 - A Back-Engineering Approach to Explore Human Mobility Patterns Across Megacities Using Online Traffic Maps

Chapter 14 - Pavement Patch Defects Detection and Classification Using Smartphones, Vibration Signals and Video Images

Chapter 15 - Collaborative Positioning for Urban Intelligent Transportation Systems (ITS) and Personal Mobility (PM): Challenges and Perspectives