Journal
COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2019 )
Volume -, Issue -, Pages 1311-1312Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3308560.3320099
Keywords
Human Mobility; Artificial Intelligence; Data Science; Generative Models; Predictive Algorithms
Categories
Funding
- EU project SoBigData RI [654024]
- EPSRC [EP/P012906/1] Funding Source: UKRI
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The inclusion of tracking technologies in personal devices opened the doors to the analysis of large sets of mobility data like GPS traces and call detail records. This tutorial presents an overview of both modeling principles of human mobility and machine learning models applicable to specific problems. We review the state of the art of five main aspects in human mobility: (1) human mobility data landscape; (2) key measures of individual and collective mobility; (3) generative models at the level of individual, population and mixture of the two; (4) next location prediction algorithms; (5) applications for social good. For each aspect, we show experiments and simulations using the Python library scikit-mobility developed by the presenters of the tutorial.
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