User location forecasting at points of interest
Proceedings of the 2012 RecSys workshop on Personalizing the local mobile experience
Understanding the Regularity and Variability of Human Mobility from Geo-trajectory
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Learning and user adaptation in location forecasting
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Time-clustering-based place prediction for wireless subscribers
IEEE/ACM Transactions on Networking (TON)
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Many applications such as management of wireless networks and the spread of mobile or biological viruses depend on modeling and predicting human mobility. However, widespread wireless localization technology, such as pervasive cell-tower/GPS location estimation, has only been available for the last few years, thus many factors that impact human mobility patterns remain under-researched. In this paper, we investigate how temporal factors impact mobility characteristics and location prediction. Our analysis of 180 mobile phone location traces show that people move farther distances, choose more unpredictable locations to visit, and have a more scattered spatial probability distribution for their location on the weekends compared to week days, or at after-work hours compared to work hours. We also analyzed location traces for a month and divided days and hours into groups for each user to obtain probability distribution of their places for each group of time intervals, and observed major improvement in future 'time-based' predictions of their location.