ビッグデータ解析による交通モデル化（第２版） Mobility Patterns, Big Data and Transport Analytics : Tools and Applications for Modeling（2）

著者名：Antoniou, Constantinos (EDT)/Dimitriou, Loukas (EDT)/Pereira, Francisco (EDT)

Elsevier（2026/03/27発売）

• 言語：ENG
• ISBN：9780443267895
• eISBN：9780443267901

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.- 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- Features a companion website with videos showing analyses performed, as well as test codes and data-sets, thus allowing readers to recreate and apply highlighted techniques to their own data

Table of Contents

1. Big data and transport analyticsPart I2. Machine Learning Fundamentals3. Using Semantic Signatures for Social Sensing in Urban Environments4. Geographic Space as a Living Structure for Predicting Human Activities Using Big Data5. Data Preparation6. Data Science and Data Visualization7. Model-Based Machine Learning for Transportation8. Capturing Travel Behavior Patterns on the Anticipating Transportation Technologies and Services9. Reinforcement Learning for Transport Applications10. Foundational principles of learner representationsPart II11. Statewide Comparison of Origin-Destination Matrices Between California Travel Model and Twitter12. Transit Data Analytics for Planning, Monitoring, Control, and Information13. A bridge between transit collective mobility patterns and fundamental economics14. Data-Driven Traffic Simulation Models: Mobility Patterns Using Machine Learning Techniques15. Big Data and Road Safety: A Comprehensive Review16. A Back-Engineering Approach to Explore Human Mobility Patterns Across Megacities Using Online Traffic Maps17. Pavement Patch Defects Detection and Classification Using Smartphones, Vibration Signals and Video Images18. Collaborative Positioning for Urban Intelligent Transportation Systems (ITS) and Personal Mobility (PM): Challenges and Perspectives19. Experiences with emerging data collection20. Machine Learning methods for processing time series count data in Transportation21. Analysing Travel Patterns on Data Collected by Bicycle Sharing Systems22. Optimal Pricing Schemes in the Maritime Market: Implementations by Deep RL23. Inequalities in mobility: Data-driven analysis of social equity issues in transport24. Conclusion