Mobility Patterns, Big Data and Transport Analytics
Tools and Applications for Modeling
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Product details:
- Edition number 2
- Publisher Elsevier Science
- Date of Publication 1 January 2026
- ISBN 9780443267895
- Binding Paperback
- No. of pages632 pages
- Size 229x152 mm
- Weight 450 g
- Language English 700
Categories
Long description:
Mobility Patterns, Big Data and Transport Analytics: Tools and Applications for Modeling, Second Edition 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. Fields covered are evolving rapidly, and this new edition updates existing material and provides new chapters that reflect recent developments in the field (such as the emergence of active, transfer and reinforcement learning).
Users will find a detailed, mobility ‘structural’ analysis and a look at the extensive behavioral characteristics of transport, observability requirements, 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.
Table of Contents:
1. Big data and transport analytics
Part I
2. Machine Learning Fundamentals
3. Using Semantic Signatures for Social Sensing in Urban Environments
4. Geographic Space as a Living Structure for Predicting Human Activities Using Big Data
5. Data Preparation
6. Data Science and Data Visualization
7. Model-Based Machine Learning for Transportation
8. Capturing Travel Behavior Patterns on the Anticipating Transportation Technologies and Services
9. Reinforcement Learning for Transport Applications
10. Foundational principles of learner representations
Part II
11. Statewide Comparison of Origin-Destination Matrices Between California Travel Model and Twitter
12. Transit Data Analytics for Planning, Monitoring, Control, and Information
13. A bridge between transit collective mobility patterns and fundamental economics
14. Data-Driven Traffic Simulation Models: Mobility Patterns Using Machine Learning Techniques
15. Big Data and Road Safety: A Comprehensive Review
16. A Back-Engineering Approach to Explore Human Mobility Patterns Across Megacities Using Online Traffic Maps
17. Pavement Patch Defects Detection and Classification Using Smartphones, Vibration Signals and Video Images
18. Collaborative Positioning for Urban Intelligent Transportation Systems (ITS) and Personal Mobility (PM): Challenges and Perspectives
19. Experiences with emerging data collection
20. Machine Learning methods for processing time series count data in Transportation
21. Analysing Travel Patterns on Data Collected by Bicycle Sharing Systems
22. Optimal Pricing Schemes in the Maritime Market: Implementations by Deep RL
23. Inequalities in mobility: Data-driven analysis of social equity issues in transport
24. Conclusion
