Handbook of Mobility Data Mining, Volume 2
Mobility Analytics and Prediction
- 1st Edition - January 27, 2023
- Editor: Haoran Zhang
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 8 4 2 4 - 6
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 8 4 2 5 - 3
Handbook of Mobility Data Mining, Volume Two: Mobility Analytics and Prediction introduces the fundamental technologies of mobile big data mining (MDM), advanced AI methods,… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteHandbook of Mobility Data Mining, Volume Two: Mobility Analytics and Prediction 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 introduces how to design MDM platforms that adapt to the evolving mobility environment and new types of transportation and users.
This helpful guide provides a basis for how to simulate and predict mobility data. After an introductory theory chapter, the book then covers crucial topics such as long-term mobility pattern analytics, mobility data generators, user information inference, Grid-based population density prediction, and more. The book concludes with a chapter on graph-based mobility data analytics. The information in this work is crucial for researchers, engineers, operators, company administrators, and policymakers in related fields, to comprehensively understand current technologies' infra-knowledge structure and limitations.
- Discusses how to efficiently simulate massive and large-scale people movement and predict mobility at an urban scale
- Introduces both online detection methods, which can sequentially process data, and offline detection methods, which are usually more robust
- 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
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- Preface
- Acknowledgments
- Chapter one. Multi-data-based travel behavior analysis and prediction
- 1. Introduction
- 2. Description of mobility big data and travel behavior
- 3. Travel behavior analysis based on mobility big data
- Chapter two. Mining individual significant places from historical trajectory data
- 1. Background
- 2. Related work
- 3. Methodology
- 4. Application
- Chapter Three. Mobility pattern clustering with big human mobility data
- 1. Introduction
- 2. Related works
- 3. Methods
- 4. Application
- Chapter Four. Change detection of travel behavior: a case study of COVID-19
- 1. Introduction
- 2. Methodologies
- 3. Results and analysis
- 4. Conclusion and discussion
- Chapter Five. User demographic characteristics inference based on big GPS trajectory data
- 1. Introduction
- 2. Preliminary
- 3. Methodology
- 4. Case study: experiment in Tokyo, Japan
- 5. Conclusion
- Chapter Six. Generative model for human mobility
- 1. Introduction
- 2. Methodology
- 3. Experiments
- 4. Conclusion
- Chapter seven. Retrieval-based human trajectory generation
- 1. Introduction
- 2. Map-matching as postprocessing
- 3. Metrics for assessment
- 4. Retrieval-based model
- 5. K-dimensional tree
- 6. Experiments
- 7. Conclusion
- Chapter eight. Grid-based origin-destination matrix prediction: a deep learning method with vector graph transformation similarity loss function
- 1. Introduction
- 2. Origin-destination matrices
- 3. Methodology
- 4. Data generation and study area
- 5. Result and discussion
- 6. Conclusion
- Chapter Nine. MetaTraj: meta-learning for cross-scene cross-object trajectory prediction
- 1. Introduction
- 2. Related works
- 3. Problem description
- 4. MetaTraj
- 5. Experiments
- 6. Conclusion
- Chapter Ten. Social-DPF: socially acceptable distribution prediction of futures
- 1. Introduction
- 2. Related works
- 3. Problem formulation
- 4. Methodology
- 5. Experiments
- 6. Conclusion
- Index
- No. of pages: 210
- Language: English
- Edition: 1
- Published: January 27, 2023
- Imprint: Elsevier
- Paperback ISBN: 9780443184246
- eBook ISBN: 9780443184253
HZ