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.- 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
Table of Contents
1. Big Data and Transport Analytics: An Introduction Constantinos Antoniou, Loukas Dimitriou and Francisco Camara Pereira1 Introduction2 Book StructurePart I: Methodological2. Machine Learning FundamentalsFrancisco Camara Pereira and Stanislav S. Borysov3. Using Semantic Signatures for Social Sensing in Urban EnvironmentsKrzysztof Janowicz, Grant McKenzie, Yingjie Hu, Rui Zhu and Song Gao4. Geographic Space as a Living Structure for Predicting Human Activities Using Big DataBin Jiang and Zheng Ren5. Data PreparationKristian Henrickson, Filipe Rodrigues and Francisco Camara Pereira6. Data Science and Data VisualizationMichalis Xyntarakis and Constantinos Antoniou7. Model-Based Machine Learning for TransportationInon Peled, Filipe Rodrigues and Francisco Camara Pereira8. Textual Data in Transportation Research: Techniques and OpportunitiesAseem Kinra, Samaneh Beheshti Kashi, Francisco Camara Pereira, Francois Combes and Werner RothengatterPart II: Applications9. Statewide Comparison of Origin-Destination Matrices Between California Travel Model and TwitterJae Hyun Lee, Adam Davis, Elizabeth McBride and Konstadinos G. Goulias10. Transit Data Analytics for Planning, Monitoring, Control, and InformationHaris N. Koutsopoulos, Zhenliang Ma, Peyman Noursalehi and Yiwen Zhu11. Data-Driven Traffic Simulation Models: Mobility Patterns Using Machine Learning TechniquesVasileia Papathanasopoulou, Constantinos Antoniou and Haris N. Koutsopoulos12. Big Data and Road Safety: A Comprehensive Review13. A Back-Engineering Approach to Explore Human Mobility Patterns Across Megacities Using Online Traffic Maps14. Pavement Patch Defects Detection and Classification Using Smartphones, Vibration Signals and Video ImagesSymeon E. Christodoulou, Charalambos Kyriakou and George Hadjidemetriou15. Collaborative Positioning for Urban Intelligent Transportation Systems (ITS) and Personal Mobility (PM): Challenges and PerspectivesVassilis Gikas, Guenther Retscher and Allison Kealy
