Mobility patterns, big data and transport analytics: tools and applications for modeling

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.

<P><b>Part A Front matter</b> 1. Introduction</p> <p><b>Part B Theoretical underpinnings</b> 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</p> <p><b>Part C Methodological</b> 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</p> <p><b>Part D Application Domains </b>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</p> <p><b>Part E Conclusions and Foresight</b> 21. Conclusions/outlook</p>