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Author Sadilek, Adam ♦ Krumm, John
Source CiteSeerX
Content type Text
Publisher AAAI Press
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Description Much work has been done on predicting where is one going to be in the immediate future, typically within the next hour. By contrast, we address the open problem of predicting human mobility far into the future, a scale of months and years. We propose an efficient nonparametric method that extracts sig-nificant and robust patterns in location data, learns their asso-ciations with contextual features (such as day of week), and subsequently leverages this information to predict the most likely location at any given time in the future. The entire pro-cess is formulated in a principled way as an eigendecompo-sition problem. Evaluation on a massive dataset with more than 32,000 days worth of GPS data across 703 diverse sub-jects shows that our model predicts the correct location with high accuracy, even years into the future. This result opens a number of interesting avenues for future research and appli-cations.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2012-01-01
Publisher Institution in ‘Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence’, AAAI