Mobility Patterns, Big Data and Transport Analytics: Tools and Applications for Modeling (Paperback)

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