Mobility Patterns, Big Data and Transport Analytics

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.

• 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

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

Part A Front matter
1. Introduction

Part B Theoretical underpinnings
2. Machine learning fundamentals
3. Combining Theory-driven and Data-driven Methods
4. Big Data Is not just a New Type, but a New Paradigm
5. Big Data Preparation Challenges and Tools
6. Data Science and Data Visualization

Part C Methodological
7. Social Networks Formations in Transport Demand Analysis
8. Human Mobility Patterns
9. Crowd-sourced data and users’ participation
10. Machine Learning Mechanisms for Augmenting Mobility Information
11. Model Based Machine Learning for the Transportation domain

Part D Application Domains
12. Capturing Mobility by Open-Data
13. Traffic Estimation Models in the Large-Scale
14. Big Data Applications in Transit Systems
15. Combining Information for Estimating Transit Ridership
16. Big Data Applications in Road Safety
17. The Mobile Society: Emerging Practices in the Travel Domain
18. Big Data in Infrastructure Management
19. Privacy and security
20. Cooperative Positioning for Urban Intelligent Transportation Systems (ITS) and Personal Mobility (PM): Challenges and Solutions

Part E Conclusions and Foresight
21. Conclusions/outlook