Data Analytics for Intelligent Transportation Systems provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems (ITS), including the tools needed to implement these methods using big data analytics and other computing techniques. The book examines the major characteristics of connected transportation systems, along with the fundamental concepts of how to analyze the data they produce. It explores collecting, archiving, processing, and distributing the data, designing data infrastructures, data management and delivery systems, and the required hardware and software technologies. It presents extensive coverage of existing and forthcoming intelligent transportation systems and data analytics technologies.
All fundamentals/concepts presented in this book are explained in the context of ITS. Users will learn everything from the basics of different ITS data types and characteristics to how to evaluate alternative data analytics for different ITS applications. They will discover how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications, along with key safety and environmental applications for both commercial and passenger vehicles, data privacy and security issues, and the role of social media data in traffic planning.
Data Analytics for Intelligent Transportation Systems will prepare an educated ITS workforce and tool builders to make the vision for safe, reliable, and environmentally sustainable intelligent transportation systems a reality. It serves as a primary or supplemental textbook for upper-level undergraduate and graduate ITS courses and a valuable reference for ITS practitioners.
1. Data Analytics for Intelligent Transportation Systems - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Dedication - p. v 6. Table of Contents - p. v 7. List of Contributors - p. xvii 8. Preface - p. xix 9. Chapter 1 Characteristics of intelligent transportation systems and its relationship with data analytics - p. 1 10. Chapter 2 Data analytics: fundamentals - p. 27 11. Chapter 3 Data science tools and techniques to support data analytics in transportation applications - p. 67 12. Chapter 4 Lifecycle and data pipelines: the centrality of data - p. 99 13. Chapter 5 Data infrastructure for connected transport systems - p. 121 14. Chapter 6 Security and data privacy of modern automobiles - p. 141 15. Chapter 7 Interactive data visualization - p. 185 16. Chapter 8 Data analytics in systems engineering for intelligent transportation systems - p. 213 17. Chapter 9 Data analytics for safety applications - p. 235 18. Chapter 10 Data analytics for intermodal freight transportation applications - p. 263 19. Chapter 11 Data analytics for prediction of real-time traffic flow parameters - p. 289 20. Chapter 12 Social media data in transportation - p. 317 21. Chapter 13 Artificial intelligence in transportation data analytics - p. 337 22. Chapter 14 Blockchains and intelligent transportation system applications - p. 383 23. Chapter 15 Detection and mitigation of spoofing attacks in-based autonomous ground vehicle navigation systems - p. 403 24. Index - p. 429
Data Analytics for Intelligent Transportation Systems provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems (ITS), including the tools needed to implement these methods using big data analytics and other computing techniques. The book examines the major characteristics of connected transportation systems, along with the fundamental concepts of how to analyze the data they produce. It explores collecting, archiving, processing, and distributing the data, designing data infrastructures, data management and delivery systems, and the required hardware and software technologies. It presents extensive coverage of existing and forthcoming intelligent transportation systems and data analytics technologies.
All fundamentals/concepts presented in this book are explained in the context of ITS. Users will learn everything from the basics of different ITS data types and characteristics to how to evaluate alternative data analytics for different ITS applications. They will discover how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications, along with key safety and environmental applications for both commercial and passenger vehicles, data privacy and security issues, and the role of social media data in traffic planning.
Data Analytics for Intelligent Transportation Systems will prepare an educated ITS workforce and tool builders to make the vision for safe, reliable, and environmentally sustainable intelligent transportation systems a reality. It serves as a primary or supplemental textbook for upper-level undergraduate and graduate ITS courses and a valuable reference for ITS practitioners.
1. Data Analytics for Intelligent Transportation Systems - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Dedication - p. v 6. Table of Contents - p. v 7. List of Contributors - p. xvii 8. Preface - p. xix 9. Chapter 1 Characteristics of intelligent transportation systems and its relationship with data analytics - p. 1 10. Chapter 2 Data analytics: fundamentals - p. 27 11. Chapter 3 Data science tools and techniques to support data analytics in transportation applications - p. 67 12. Chapter 4 Lifecycle and data pipelines: the centrality of data - p. 99 13. Chapter 5 Data infrastructure for connected transport systems - p. 121 14. Chapter 6 Security and data privacy of modern automobiles - p. 141 15. Chapter 7 Interactive data visualization - p. 185 16. Chapter 8 Data analytics in systems engineering for intelligent transportation systems - p. 213 17. Chapter 9 Data analytics for safety applications - p. 235 18. Chapter 10 Data analytics for intermodal freight transportation applications - p. 263 19. Chapter 11 Data analytics for prediction of real-time traffic flow parameters - p. 289 20. Chapter 12 Social media data in transportation - p. 317 21. Chapter 13 Artificial intelligence in transportation data analytics - p. 337 22. Chapter 14 Blockchains and intelligent transportation system applications - p. 383 23. Chapter 15 Detection and mitigation of spoofing attacks in-based autonomous ground vehicle navigation systems - p. 403 24. Index - p. 429
