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Predicting human behaviour from selected mobile phone data points

29 citations0/0 files storedACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)

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Human Mobility and Location-Based Analysis Context-Aware Activity Recognition Systems Opportunistic and Delay-Tolerant Networks

Abstract

The mobile phone offers a unique opportunity to predict a person's behaviour automatically for advanced ubiquitous services. In this note, we analyse cellular data collected as part of the Reality Mining project and use information-theoretic concepts to answer three questions (i) What time points in the day help predict a mobile phone user's activity at another time point? (ii) What time points in history are most useful to predict his future activities? and (iii) How difficult is it to predict his activity at a given time from another user's activity at another time?

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References

2005Reality mining: sensing complex social systems 2009Eigenbehaviors: identifying structure in routine 2006Predestination: Inferring Destinations from Partial Trajectories 2008What did you do today?2006Periodic properties of user mobility and access-point popularity

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2013Effective routine behavior pattern discovery from sparse mobile phone data via collaborative filtering 2013A next location prediction method for smartphones using blockmodels 2014Effective Mobile Context Pattern Discovery via Adapted Hierarchical Dirichlet Processes 2017Usage of Smartphone Data to Derive an Indicator for Collaborative Mobility between Individuals 2012Classification and prediction of whereabouts patterns from the Reality Mining dataset 2018Using Passive Data Collection Methods to Learn Complex Mobility Patterns: An Exploratory Analysis

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