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Mobility Patterns, Big Data and Transport Analytics

Tools and Applications for Modeling

Specificaties
Paperback, blz. | Engels
Elsevier Science | e druk, 2026
ISBN13: 9780443267895
Rubricering
Elsevier Science e druk, 2026 9780443267895
€ 141,39
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Samenvatting

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.

Specificaties

ISBN13:9780443267895
Taal:Engels
Bindwijze:Paperback
Herdrukdatum:27-3-2026

Inhoudsopgave

1. Big data and transport analytics<br><br>Part I<br>2. Machine Learning Fundamentals<br>3. Using Semantic Signatures for Social Sensing in Urban Environments<br>4. Geographic Space as a Living Structure for Predicting Human Activities Using Big Data<br>5. Data Preparation<br>6. Data Science and Data Visualization<br>7. Model-Based Machine Learning for Transportation<br>8. Capturing Travel Behavior Patterns on the Anticipating Transportation Technologies and Services<br>9. Reinforcement Learning for Transport Applications<br>10. Foundational principles of learner representations<br><br>Part II<br>11. Statewide Comparison of Origin-Destination Matrices Between California Travel Model and Twitter<br>12. Transit Data Analytics for Planning, Monitoring, Control, and Information<br>13. A bridge between transit collective mobility patterns and fundamental economics<br>14. Data-Driven Traffic Simulation Models: Mobility Patterns Using Machine Learning Techniques<br>15. Big Data and Road Safety: A Comprehensive Review<br>16. A Back-Engineering Approach to Explore Human Mobility Patterns Across Megacities Using Online Traffic Maps<br>17. Pavement Patch Defects Detection and Classification Using Smartphones, Vibration Signals and Video Images<br>18. Collaborative Positioning for Urban Intelligent Transportation Systems (ITS) and Personal Mobility (PM): Challenges and Perspectives<br>19. Experiences with emerging data collection<br>20. Machine Learning methods for processing time series count data in Transportation<br>21. Analysing Travel Patterns on Data Collected by Bicycle Sharing Systems<br>22. Optimal Pricing Schemes in the Maritime Market: Implementations by Deep RL<br>23. Inequalities in mobility: Data-driven analysis of social equity issues in transport<br>24. Conclusion
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        Mobility Patterns, Big Data and Transport Analytics