Mobility Patterns, Big Data and Transport Analytics: Tools and Applications for Modeling
Editat de Constantinos Antoniou, Loukas Dimitriou, Francisco Pereiraen Limba Engleză Paperback – 27 noi 2018
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
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Specificații
ISBN-13: 9780128129708
ISBN-10: 0128129700
Pagini: 452
Dimensiuni: 152 x 229 x 23 mm
Greutate: 0.6 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0128129700
Pagini: 452
Dimensiuni: 152 x 229 x 23 mm
Greutate: 0.6 kg
Editura: ELSEVIER SCIENCE
Public țintă
1 – Transport researchers, practitioners, and consultants, 2 – Undergraduate and graduate students in transportation programs; 3 – Transport policy makersCuprins
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
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