Prediction of Routes between Significant Locations Based on Personal GPS Data

Mobile devices equipped with various sensors have the potential of providing context-aware services. Location is one of the most common forms of context, which can be applied to diverse applications. In this paper, we present methods for learning and predicting users' routes between significant locations, e.g., home and workplaces, based on personal GPS data. A user's significant locations and routes between them are learned by a set of rules as well as clustering. When the user is moving, our methods can predict which of the learned routes is being taken now. After the route prediction, the user's next location can also be inferred. Our methods have been applied to the real GPS datasets from four subjects. For the next location prediction task, the achieved accuracy was 84.8%.