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.
1. Chapter 1. Big data and transport analytics: an introduction - p. 1 2. Chapter 2. Machine learning fundamentals - p. 9 3. Chapter 3. Using semantic signatures for social sensing in urban environments - p. 35 4. Chapter 4. Geographic space as a living structure for predicting human activities using big data - p. 57 5. Chapter 5. Data preparation - p. 75 6. Chapter 6. Data Science and Data Visualization - p. 117 7. Chapter 7. Model-based machine learning for transportation - p. 151 8. Chapter 8. Reinforcement learning: a snapshot bibliometric analysis of the current state-of-the-art - p. 183 9. Chapter 9. Foundational principles for representation learning: applications in autonomous mobility - p. 265 10. Chapter 10. Large language models for urban transportation - p. 287 11. Chapter 11. Capturing travel behavior patterns on the anticipating transportation technologies and services - p. 319 12. Chapter 12. Statewide comparison of origin-destination matrices between California travel model and Twitter - p. 393 13. Chapter 13. User-centric transit data analytics for inference, prediction, and optimization - p. 421 14. Chapter 14. Transit systems and collective mobility patterns: a microeconomic approach using simultaneous equations modeling - p. 463 15. Chapter 15. Data-driven traffic prediction and simulation models: mobility patterns using machine learning techniques - p. 513 16. Chapter 16. Big Data and road safety: a comprehensive review - p. 559 17. Chapter 17. Mobility patterns across multiple cities based on online traffic maps - p. 615 18. Chapter 18. Pavement patch defects detection and classification using smartphones, vibration signals, and video images - p. 637 19. Chapter 19. Enabling smart mobility: collaborative positioning for urban intelligent transportation systems and personal mobility - p. 661 20. Chapter 20. Emerging methods to collect big data on travel experiences - p. 701 21. Chapter 21. Machine learning methods for processing time series count data in transportation - p. 725 22. Chapter 22. Micromobility spatial and temporal patterns within the urban landscape: considerations on demand modeling, the effects of mobility safety and transport disruptions - p. 763 23. Chapter 23. Optimal static and dynamic tariff-setting strategies in systems of port terminals using reinforcement learning methods - p. 787 24. Chapter 24. Inequalities in mobility: data-driven analysis of social equity issues in transport - p. 817 25. Index - p. 843
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.
1. Chapter 1. Big data and transport analytics: an introduction - p. 1 2. Chapter 2. Machine learning fundamentals - p. 9 3. Chapter 3. Using semantic signatures for social sensing in urban environments - p. 35 4. Chapter 4. Geographic space as a living structure for predicting human activities using big data - p. 57 5. Chapter 5. Data preparation - p. 75 6. Chapter 6. Data Science and Data Visualization - p. 117 7. Chapter 7. Model-based machine learning for transportation - p. 151 8. Chapter 8. Reinforcement learning: a snapshot bibliometric analysis of the current state-of-the-art - p. 183 9. Chapter 9. Foundational principles for representation learning: applications in autonomous mobility - p. 265 10. Chapter 10. Large language models for urban transportation - p. 287 11. Chapter 11. Capturing travel behavior patterns on the anticipating transportation technologies and services - p. 319 12. Chapter 12. Statewide comparison of origin-destination matrices between California travel model and Twitter - p. 393 13. Chapter 13. User-centric transit data analytics for inference, prediction, and optimization - p. 421 14. Chapter 14. Transit systems and collective mobility patterns: a microeconomic approach using simultaneous equations modeling - p. 463 15. Chapter 15. Data-driven traffic prediction and simulation models: mobility patterns using machine learning techniques - p. 513 16. Chapter 16. Big Data and road safety: a comprehensive review - p. 559 17. Chapter 17. Mobility patterns across multiple cities based on online traffic maps - p. 615 18. Chapter 18. Pavement patch defects detection and classification using smartphones, vibration signals, and video images - p. 637 19. Chapter 19. Enabling smart mobility: collaborative positioning for urban intelligent transportation systems and personal mobility - p. 661 20. Chapter 20. Emerging methods to collect big data on travel experiences - p. 701 21. Chapter 21. Machine learning methods for processing time series count data in transportation - p. 725 22. Chapter 22. Micromobility spatial and temporal patterns within the urban landscape: considerations on demand modeling, the effects of mobility safety and transport disruptions - p. 763 23. Chapter 23. Optimal static and dynamic tariff-setting strategies in systems of port terminals using reinforcement learning methods - p. 787 24. Chapter 24. Inequalities in mobility: data-driven analysis of social equity issues in transport - p. 817 25. Index - p. 843
