Handbook of Mobility Data Mining, Volume 3
Mobility Data-Driven Applications
- 1st Edition - January 26, 2023
- Imprint: Elsevier
- Editor: Haoran Zhang
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 5 8 9 2 - 9
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 8 4 2 3 - 9
Handbook of Mobility Data Mining: Volume Three: Mobility Data-Driven Applications introduces the fundamental technologies of mobile big data mining (MDM), advanced AI methods,… Read more
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Request a sales quoteHandbook 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.
- Introduces MDM applications from six major areas: intelligent transportation management, shared transportation systems, disaster management, pandemic response, low-carbon transportation, and social equality
- Uses case studies to examine possible solutions that facilitate ethical, secure, and controlled emergency management based on mobile big data
- Helps develop policy innovations beneficial to citizens, businesses, and society
- Stems from the editor’s strong network of global transport authorities and transport companies, providing a solid knowledge structure and data foundation as well as geographical and stakeholder coverage
Researchers, engineers, operators, company administrators, and policymakers on transportation, environment, urban planning, data mining, and sustainability; Transport-mobility planners, the road and vehicle industry, urban management authorities, transportation institutes, traffic police, public and goods transport operators; masters and Ph.D. students pursuing research in the area of mobility and transportation
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- Preface
- Acknowledgments
- Chapter One. Mobility data in bike-sharing systems
- 1. Introduction
- 2. Literature review
- 3. Case study
- 4. Conclusion
- Chapter Two. Improvement of an online ride-hailing system based on empirical GPS data
- 1. Introduction
- 2. Related works
- 3. Problem description
- 4. Study case
- 5. Methodology
- 6. Result analysis
- 7. Conclusion and future work
- Chapter Three. Research on vehicle routing problem and application scenarios
- 1. Introduction
- 2. Vehicle routing problem in MaaS shared-bus system
- 3. Application Scenario of Vehicle Routing Problem in Logistics Transportation
- 4. Summary and prospect
- Chapter Four. Travel demand prediction model and applications
- 1. Introduction
- 2. Deep learning
- 3. Travel demand prediction model
- 4. Study case
- 5. Result and discussion
- 6. Technical potential analysis of travel demand prediction model
- 7. Conclusion
- Chapter five. Railway usage behavior analysis based on mobile phone big data
- 1. Introduction
- 2. Literature review
- 3. Methodology
- 4. Case study
- 5. Discussion and conclusions
- Chapter Six. An Origin-Destination matrix prediction-based road dynamic pricing optimization system
- 1. Introduction
- 2. Methodology
- 3. Study case
- 4. Result and discussion
- 5. Conclusion
- Chapter Seven. Blockchain for location-based big data-driven services
- 1. Introduction
- 2. Background of blockchain
- 3. Location-based big data-driven services
- 4. Blockchain in location-based big data-driven services
- 5. Future trend of blockchain
- Chapter Eight. Mobility data in urban road emission mitigation
- 1. Introduction
- 2. Literature review
- 3. Case studies
- 4. Conclusion
- Chapter Nine. Living environment inequity analyses based on mobile phone big data
- 1. Introduction
- 2. Framework and dataset
- 3. Methodology
- 4. Results
- 5. Conclusion and limitation
- Index
- Edition: 1
- Published: January 26, 2023
- Imprint: Elsevier
- No. of pages: 242
- Language: English
- Paperback ISBN: 9780323958929
- eBook ISBN: 9780443184239
HZ
Haoran Zhang
Haoran (Ronan) Zhang is Assistant Professor in the Center for Spatial Information Science at the University of Tokyo, a Researcher at the School of Business Society and Engineering at Mälardalen University in Sweden, and Senior Scientist at Locationmind Inc. in Japan. His research includes smart supply chain technologies, GPS data in shared transportation, urban sustainable performance, GIS technologies in renewable energy systems, and smart cities. He is author of numerous journal articles and Editorial Board Member of several international academic journals. He has Ph.D.’s in both Engineering and Sociocultural Environment and was awarded Excellent Young Researcher by Japan’s Ministry of Education, Culture, Sports, Science and Technology.
Affiliations and expertise
Assistant Professor, Center for Spatial Information Science, University of Tokyo, Tokyo, Japan; Researcher, School of Business Society and Engineering, Mälardalen University, Sweden; Senior Scientist, Locationmind Inc., Tokyo, JapanRead Handbook of Mobility Data Mining, Volume 3 on ScienceDirect