Handbook of mobility data mining. Volume Two, Mobility analytics and prediction / edited by Haoran Zhang.

Contributor(s): Zhang, Haoran (College teacher) [editor.]Material type: TextTextPublisher: Amsterdam, Netherlands ; Oxford, United Kingdom ; Cambridge MA : Elsevier, [2023]Copyright date: �2023Description: 1 online resource : illustrations (some color)Content type: text | still image Media type: computer Carrier type: online resourceISBN: 0443184259; 9780443184253Other title: Mobility analytics and predictionSubject(s): Big data | Mobile communication systemsAdditional physical formats: Print version:: No titleDDC classification: 005.7 LOC classification: QA76.9.B45 | H352 2023Online resources: ScienceDirect
Contents:
Chapter one - Multi-data-based travel behavior analysis and prediction -- Chapter two - Mining individual significant places from historical trajectory data -- Chapter Three - Mobility pattern clustering with big human mobility data -- Chapter Four - Change detection of travel behavior: a case study of COVID-19 -- Chapter Five - User demographic characteristics inference based on big GPS trajectory data -- Chapter Six - Generative model for human mobility -- Chapter seven - Retrieval-based human trajectory generation -- Chapter eight - Grid-based origin-destination matrix prediction: a deep learning method with vector graph transformation similarity loss function -- Chapter Nine - MetaTraj: meta-learning for cross-scene cross-object trajectory prediction -- Chapter Ten - Social-DPF: socially acceptable distribution prediction of futures.
Summary: "Handbook 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."--Provided by Publisher.
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Chapter one - Multi-data-based travel behavior analysis and prediction -- Chapter two - Mining individual significant places from historical trajectory data -- Chapter Three - Mobility pattern clustering with big human mobility data -- Chapter Four - Change detection of travel behavior: a case study of COVID-19 -- Chapter Five - User demographic characteristics inference based on big GPS trajectory data -- Chapter Six - Generative model for human mobility -- Chapter seven - Retrieval-based human trajectory generation -- Chapter eight - Grid-based origin-destination matrix prediction: a deep learning method with vector graph transformation similarity loss function -- Chapter Nine - MetaTraj: meta-learning for cross-scene cross-object trajectory prediction -- Chapter Ten - Social-DPF: socially acceptable distribution prediction of futures.

"Handbook 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."--Provided by Publisher.

Includes bibliographical references and index.

Description based on online resource; title from digital title page (viewed on March 23, 2023).

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