Modeling User Activity Patterns for Next-Place Prediction

Location has played a very important role in pervasive computing systems. Beyond the current location, knowing an individual's next location in advance can also enable many novel mobile applications and services such as targeted advertising and the smooth handover between two separate networks. Although extensive studies about location prediction have been carried out, the existing prediction methods either encounter “cold start” problems when an individual's trajectory data are sparse or erratically perform when an individual performs activities in a new region. In this paper, we propose a novel approach based on the activity pattern for location prediction. Instead of directly predicting an individual's next location, we first infer the individual's next activity by modeling user activity patterns, and then, we predict his/her next location on the basis of the inferred next activity. Using real-life trajectory data, we demonstrate that the proposed approach can realize the smooth upgrade of the prediction performance and perform robustly.