Inferring Movement Trajectories From Gps Snippets

Inferring movement trajectories can be a challenging task, in particular when detailed tracking information is not available due to privacy and data collection constraints. In this paper we present a complete and computationally tractable model for estimating and predicting trajectories based on sparsely sampled, anonymous GPS land-marks that we call GPS snippets. To combat data sparsity we use mapping data as side information to constrain the inference process. We show the efficacy of our approach on a set of prediction tasks over data collected from different cities in the US.