Handbook of mobility data mining. Volume Two, Mobility analytics and prediction /
Mobility analytics and prediction
edited by Haoran Zhang.
- 1 online resource : illustrations (some color)
Includes bibliographical references and index.
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.