Future Locations Prediction with Uncertain Data

Abstract

The ability to predict future movements for moving objects enables better decisions in terms of time, cost, and impact on the environment. Unfortunately, future location prediction is a challenging task. Existing works exploit techniques to predict a trip destination, but they are effective only when location data are precise (e.g., GPS data) and movements are observed over long periods of time (e.g., weeks). We introduce a data mining approach based on a Hidden Markov Model (HMM) that overcomes these limits and improves existing results in terms of precision of the prediction, for both the route (i.e., trajectory) and the final destination. The model is resistant to uncertain location data, as it works with data collected by using cell-towers to localize the users instead of GPS devices, and reaches good prediction results in shorter times (days instead of weeks in a representative real-world application). Finally, we introduce an enhanced version of the model that is orders of magnitude faster than the standard HMM implementation.