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
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
Constantinos Antoniou is a Professor and Chair of Transportation Systems Engineering at the Technical University of Munich, Germany. He was previously an Associate Professor at the National Technical University of Athens, Greece. His research focuses on modelling and simulation of transportation systems, Intelligent Transport Systems (ITS), calibration and optimization applications, road safety and sustainable transport system. Antoniou has been involved in a large number of projects, primarily in Europe and the US, and has authored more than 250 scientific publications, including in Elsevier's Transportation Research Part C: Emerging Technologies (for which he serves on the editorial board), and Journal of Transport Geography. He has also authored a book on dynamic traffic assignment models
Dr. Loukas Dimitriou is an Assistant Professor in Dept. of Civil and Environmental Engineering, University of Cyprus (UCY) and founder and head of the L?B for Transport Engineering, UCY. His research interests focuses in the application of advanced computational intelligence methods, concepts and techniques for understanding the complex phenomena involved in realistic transport systems and further, developing design and control strategies such as to optimize their performance. The methodological paradigms that he propose utilize (or combine) elements from Data Science, behavioural analytics, complex systems modelling and advanced optimization, applied in traditional fields of transport, like demand modelling, travel behaviour and systems organization, optimization and control. He has more than 100 publications in peer-reviewed journals, proceedings of conferences and book chapters, while he is an active member of international scientific organizations and committees.
Transport Infrastructure Planning and Design
Demand Analysis and Forecasting
Intelligent Transport Systems
Francisco Pereira is a Professor at the Technical University of Denmark, in Kongens Lyngby, Denmark, where he leads the Smart Mobility research group. Previously, he was Senior Research Scientist at MIT/CEE ITSLab, where he worked on real-time traffic prediction, behavior modeling, and advanced data collection technologies, both in Boston and Singapore, as part of the Singapore-MIT Alliance for Research and Technology, Future Urban Mobility project (SMART/FM). His main research focus is on applying machine learning and pattern recognition to the context of transportation systems with the purpose of understanding and predicting mobility behavior, and modeling and optimizing the transportation system as a whole. He has been published in many journals, including in Elsevier's Transportation Research Part C: Emerging Technologies.