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.
1. Chapter 1: Big Data and Transport Analytics: An Introduction - p. xix 2. Part I: Methodological - p. 5 3. Chapter 2: Machine Learning Fundamentals - p. 5 4. Chapter 3: Using Semantic Signatures for Social Sensing in Urban Environments - p. 29 5. Chapter 4: Geographic Space as a Living Structure for Predicting Human Activities Using Big Data - p. 54 6. Chapter 5: Data Preparation - p. 72 7. Chapter 6: Data Science and Data Visualization - p. 106 8. Chapter 7: Model-Based Machine Learning for Transportation - p. 144 9. Chapter 8: Textual Data in Transportation Research: Techniques and Opportunities - p. 171 10. Part II: Applications - p. 197 11. Chapter 9: Statewide Comparison of Origin-Destination Matrices Between California Travel Model and Twitter - p. 197 12. Chapter 10: Transit Data Analytics for Planning, Monitoring, Control, and Information - p. 228 13. Chapter 11: Data-Driven Traffic Simulation Models: Mobility Patterns Using Machine Learning Techniques - p. 261 14. Chapter 12: Big Data and Road Safety: A Comprehensive Review - p. 295 15. Chapter 13: A Back-Engineering Approach to Explore Human Mobility Patterns Across Megacities Using Online Traffic Maps - p. 343 16. Chapter 14: Pavement Patch Defects Detection and Classification Using Smartphones, Vibration Signals and Video Images - p. 363 17. Chapter 15: Collaborative Positioning for Urban Intelligent Transportation Systems (ITS) and Personal Mobility (PM): Challenges and Perspectives - p. 380 18. Conclusions - p. 415 19. Index - p. 419
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.
1. Chapter 1: Big Data and Transport Analytics: An Introduction - p. xix 2. Part I: Methodological - p. 5 3. Chapter 2: Machine Learning Fundamentals - p. 5 4. Chapter 3: Using Semantic Signatures for Social Sensing in Urban Environments - p. 29 5. Chapter 4: Geographic Space as a Living Structure for Predicting Human Activities Using Big Data - p. 54 6. Chapter 5: Data Preparation - p. 72 7. Chapter 6: Data Science and Data Visualization - p. 106 8. Chapter 7: Model-Based Machine Learning for Transportation - p. 144 9. Chapter 8: Textual Data in Transportation Research: Techniques and Opportunities - p. 171 10. Part II: Applications - p. 197 11. Chapter 9: Statewide Comparison of Origin-Destination Matrices Between California Travel Model and Twitter - p. 197 12. Chapter 10: Transit Data Analytics for Planning, Monitoring, Control, and Information - p. 228 13. Chapter 11: Data-Driven Traffic Simulation Models: Mobility Patterns Using Machine Learning Techniques - p. 261 14. Chapter 12: Big Data and Road Safety: A Comprehensive Review - p. 295 15. Chapter 13: A Back-Engineering Approach to Explore Human Mobility Patterns Across Megacities Using Online Traffic Maps - p. 343 16. Chapter 14: Pavement Patch Defects Detection and Classification Using Smartphones, Vibration Signals and Video Images - p. 363 17. Chapter 15: Collaborative Positioning for Urban Intelligent Transportation Systems (ITS) and Personal Mobility (PM): Challenges and Perspectives - p. 380 18. Conclusions - p. 415 19. Index - p. 419
