NextPlace: a spatio-temporal prediction framework for pervasive systems

  • Authors:

  • Salvatore Scellato;Mirco Musolesi;Cecilia Mascolo;Vito Latora;Andrew T. Campbell

  • Affiliations:

  • Computer Laboratory, University of Cambridge, UK;School of Computer Science, University of St. Andrews, UK;Computer Laboratory, University of Cambridge, UK;Dipartimento di Fisica, University of Catania, Italy;Department of Computer Science, Dartmouth College

  • Venue:
  • Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
  • Year:
  • 2011

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Abstract

Accurate and fine-grained prediction of future user location and geographical profile has interesting and promising applications including targeted content service, advertisement dissemination for mobile users, and recreational social networking tools for smart-phones. Existing techniques based on linear and probabilistic models are not able to provide accurate prediction of the location patterns from a spatio-temporal perspective, especially for long-term estimation. More specifically, they are able to only forecast the next location of a user, but not his/her arrival time and residence time, i.e., the interval of time spent in that location. Moreover, these techniques are often based on prediction models that are not able to extend predictions further in the future. In this paper we present NextPlace, a novel approach to location prediction based on nonlinear time series analysis of the arrival and residence times of users in relevant places. NextPlace focuses on the predictability of single users when they visit their most important places, rather than on the transitions between different locations. We report about our evaluation using four different datasets and we compare our forecasting results to those obtained by means of the prediction techniques proposed in the literature. We show how we achieve higher performance compared to other predictors and also more stability over time, with an overall prediction precision of up to 90% and a performance increment of at least 50% with respect to the state of the art.