User Location Forecasting at Points of Interest

Predicting the location of a mobile user in the near future can be used for a very large number of user-centered or crowd-centered ubiquitous applications. It is convenient for the discussion to think in terms of discrete locations driven by Points of Interest (POI) instead of absolute positions. We postulate that POI sequences are Markovian once the data is clustered by day of the week and time of the day. To prove our hypothesis we used a public dataset, used in a previous work [15]. In that paper the authors were able to predict the location of a user with 90 % to 70 % accuracy in five minutes and one hour time windows, respectively. With our approach, using Hidden Markov Models, we are able to predict the next POIs within seven hours without signifi-cant accuracy decrease. This result enables a large number of potential applications where the aggregate data of a single users conform the behavior of the crowd