Mobility Patterns, Big Data and Transport Analytics

Tools and Applications for Modeling
Herausgegeben:Antoniou, Constantinos; Dimitriou, Loukas; Pereira, Francisco

• Broschiertes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Elsevier / Elsevier Science & Technology
• Artikelnr. des Verlages: C2016-0-03572-6
• Englisch
• Abmessung: 20mm x 152mm x 229mm
• Gewicht: 680g
• ISBN-13: 9780128129708
• Artikelnr.: 51390128

Inhaltsangabe

1. Big Data and Transport Analytics: An Introduction Constantinos Antoniou, Loukas Dimitriou and Francisco Camara Pereira
1 Introduction
2 Book Structure
Part I: Methodological
2. Machine Learning Fundamentals
Francisco Camara Pereira and Stanislav S. Borysov
3. Using Semantic Signatures for Social Sensing in Urban Environments
Krzysztof Janowicz, Grant McKenzie, Yingjie Hu, Rui Zhu and Song Gao
4. Geographic Space as a Living Structure for Predicting Human Activities Using Big Data
Bin Jiang and Zheng Ren
5. Data Preparation
Kristian Henrickson, Filipe Rodrigues and Francisco Camara Pereira
6. Data Science and Data Visualization
Michalis Xyntarakis and Constantinos Antoniou
7. Model-Based Machine Learning for Transportation
Inon Peled, Filipe Rodrigues and Francisco Camara Pereira
8. Textual Data in Transportation Research: Techniques and Opportunities
Aseem Kinra, Samaneh Beheshti Kashi, Francisco Camara Pereira, Francois Combes and Werner Rothengatter
Part II: Applications
9. Statewide Comparison of Origin-Destination Matrices Between California Travel Model and Twitter
Jae Hyun Lee, Adam Davis, Elizabeth McBride and Konstadinos G. Goulias
10. Transit Data Analytics for Planning, Monitoring, Control, and Information
Haris N. Koutsopoulos, Zhenliang Ma, Peyman Noursalehi and Yiwen Zhu
11. Data-Driven Traffic Simulation Models: Mobility Patterns Using Machine Learning Techniques
Vasileia Papathanasopoulou, Constantinos Antoniou and Haris N. Koutsopoulos
12. Big Data and Road Safety: A Comprehensive Review
13. A Back-Engineering Approach to Explore Human Mobility Patterns Across Megacities Using Online Traffic Maps
14. Pavement Patch Defects Detection and Classification Using Smartphones, Vibration Signals and Video Images
Symeon E. Christodoulou, Charalambos Kyriakou and George Hadjidemetriou
15. Collaborative Positioning for Urban Intelligent Transportation Systems (ITS) and Personal Mobility (PM): Challenges and Perspectives
Vassilis Gikas, Guenther Retscher and Allison Kealy