Handbook of Mobility Data Mining, Volume 1
Data Preprocessing and Visualization
- 1st Edition - January 26, 2023
- Imprint: Elsevier
- Editor: Haoran Zhang
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 8 4 2 8 - 4
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 8 4 2 9 - 1
Handbook of Mobility Data Mining, Volume One: Data Preprocessing and Visualization 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 One: Data Preprocessing and Visualization 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.
Further, the book introduces how to design MDM platforms that adapt to the evolving mobility environment, 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 volume focuses on how to efficiently pre-process mobile big data to extract and utilize critical feature information of high-dimensional city people flow. The book first provides a conceptual theory and framework, then discusses data sources, trajectory map-matching, noise filtering, trajectory data segmentation, data quality assessment, and more, concluding with a chapter on privacy protection in mobile big data mining.
- Introduces the characteristics of different mobility data sources, like GPS, CDR, and sensor-based mobility data
- Summarizes existing visualization technologies of the current transportation system into a multi-view frame, covering the perspective of the three leading actors
- Provides recommendations for practical open-source tools and libraries for system visualization
- 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. An overview of urban data variety and respective value to urban computing
- 1. Introduction
- 2. Urban data variety and value
- 3. Conclusion
- Chapter Two. Quality assessment for big mobility data
- 1. Introduction
- 2. Trajectory similarity
- 3. Travel pattern similarity
- 4. Origin-destination matrix similarity
- 5. Conclusion and future directions
- Chapter Three. Noise filter method for mobile trajectory data
- 1. Introduction
- 2. Simple data cleaning
- 3. Mean filter and median filter
- 4. Kalman filter
- 5. Particle filter
- 6. Road network matching
- 7. An example of mobile trajectory data noise filter
- Chapter Four. Modifiable areal unit problem in grided population density map
- 1. Introduction
- 2. Error analysis
- 3. Real case experiment
- 4. Conclusion
- Chapter Five. Few-shot count estimation of mobility dynamics by scaling GPS
- 1. Introduction
- 2. Related works
- 3. Methodology
- 4. Experiments
- 5. Conclusion
- Chapter Six. Trip segmentation and mode detection for human mobility data
- 1. Introduction
- 2. Hidden Markov Model
- 3. Model training
- 4. Decoding
- 5. Application
- Chapter Seven. Benchmark of travel mode detection with smartphone GPS trajectories
- 1. Introduction
- 2. Ground truth data collection
- 3. Method for travel mode detection
- 4. Case study
- 5. Conclusion
- Chapter Eight. Trajectory super-resolution methods
- 1. Introduction
- 2. Related work
- 3. Preliminary
- 4. Data description
- 5. Baseline methods
- 6. Experiments and results
- 7. Conclusion and discussion
- Chapter Nine. Map-matching for low accuracy trajectory data
- 1. Introduction
- 2. Traditional map-matching method
- 3. Multi-steps least cost algorithm
- 4. Real world application
- Chapter Ten. Social information labeling for individual mobile phone user
- 1. Background
- 2. Data description
- 3. Framework and case study
- 4. Methodology
- 5. Evaluation metrics
- 6. Result
- Chapter eleven. Web-based spatio-temporal data visualization technology for urban digital twin
- 1. Introduction
- 2. Web-based data visualization technology
- 3. Visualization of data
- 4. Case of web-based urban digital twin application: 3D UrbanMOB
- 5. Conclusion
- Index
- Edition: 1
- Published: January 26, 2023
- Imprint: Elsevier
- No. of pages: 222
- Language: English
- Paperback ISBN: 9780443184284
- eBook ISBN: 9780443184291
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 1 on ScienceDirect