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.
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.
