Handbook of Mobility Data Mining: Volume Three: Mobility Data-Driven Applications introduces the fundamental technologies of mobile big data mining (MDM), advanced AI methods, and upper-level applications, helping readers comprehensively understand MDM with a bottom-up approach. The book explains how to preprocess mobile big data, visualize urban mobility, simulate and predict human travel behavior, and assess urban mobility characteristics and their matching performance as conditions and constraints in transport, emergency management, and sustainability development systems. The book contains crucial information for researchers, engineers, operators, administrators, and policymakers seeking greater understanding of current technologies' infra-knowledge structure and limitations.
The book introduces how to design MDM platforms that adapt to the evolving mobility environment—and new types of transportation and users—based on an integrated solution that utilizes sensing and communication capabilities to tackle significant challenges faced by the MDM field. This third volume looks at various cases studies to illustrate and explore the methods introduced in the first two volumes, covering topics such as Intelligent Transportation Management, Smart Emergency Management—detailing cases such as the Fukushima earthquake, Hurricane Katrina, and COVID-19—and Urban Sustainability Development, covering bicycle and railway travel behavior, mobility inequality, and road and light pollution inequality.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. List of contributors - p. ix 7. Preface - p. xi 8. Acknowledgments - p. xii 9. Chapter One. Mobility data in bike-sharing systems - p. 1 10. Chapter Two. Improvement of an online ride-hailing system based on empirical GPS data - p. 23 11. Chapter Three. Research on vehicle routing problem and application scenarios - p. 63 12. Chapter Four. Travel demand prediction model and applications - p. 89 13. Chapter five. Railway usage behavior analysis based on mobile phone big data - p. 113 14. Chapter Six. An Origin-Destination matrix prediction-based road dynamic pricing optimization system - p. 136 15. Chapter Seven. Blockchain for location-based big data-driven services - p. 153 16. Chapter Eight. Mobility data in urban road emission mitigation - p. 173 17. Chapter Nine. Living environment inequity analyses based on mobile phone big data - p. 195 18. Index - p. 223
Handbook of Mobility Data Mining: Volume Three: Mobility Data-Driven Applications introduces the fundamental technologies of mobile big data mining (MDM), advanced AI methods, and upper-level applications, helping readers comprehensively understand MDM with a bottom-up approach. The book explains how to preprocess mobile big data, visualize urban mobility, simulate and predict human travel behavior, and assess urban mobility characteristics and their matching performance as conditions and constraints in transport, emergency management, and sustainability development systems. The book contains crucial information for researchers, engineers, operators, administrators, and policymakers seeking greater understanding of current technologies' infra-knowledge structure and limitations.
The book introduces how to design MDM platforms that adapt to the evolving mobility environment—and new types of transportation and users—based on an integrated solution that utilizes sensing and communication capabilities to tackle significant challenges faced by the MDM field. This third volume looks at various cases studies to illustrate and explore the methods introduced in the first two volumes, covering topics such as Intelligent Transportation Management, Smart Emergency Management—detailing cases such as the Fukushima earthquake, Hurricane Katrina, and COVID-19—and Urban Sustainability Development, covering bicycle and railway travel behavior, mobility inequality, and road and light pollution inequality.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. List of contributors - p. ix 7. Preface - p. xi 8. Acknowledgments - p. xii 9. Chapter One. Mobility data in bike-sharing systems - p. 1 10. Chapter Two. Improvement of an online ride-hailing system based on empirical GPS data - p. 23 11. Chapter Three. Research on vehicle routing problem and application scenarios - p. 63 12. Chapter Four. Travel demand prediction model and applications - p. 89 13. Chapter five. Railway usage behavior analysis based on mobile phone big data - p. 113 14. Chapter Six. An Origin-Destination matrix prediction-based road dynamic pricing optimization system - p. 136 15. Chapter Seven. Blockchain for location-based big data-driven services - p. 153 16. Chapter Eight. Mobility data in urban road emission mitigation - p. 173 17. Chapter Nine. Living environment inequity analyses based on mobile phone big data - p. 195 18. Index - p. 223
