Pathlet learning for compressing and planning trajectories

The wide deployment of GPS devices has generated gigantic datasets of pedestrian and vehicular trajectories. These datasets offer great opportunities for enhancing our understanding of human mobility patterns, thus benefiting many applications ranging from location-based services (LBS) to transportation system planning. In this work, we introduce the notion of pathlet for the purpose of compressing and planning trajectories. Given a collection of trajectories on a roadmap as input, we seek to compute a compact dictionary of pathlets so that the number of pathlets that are used to represent each trajectory is minimized. We propose an effective approach whose complexity is linear in the number of trajectories. Experimental results show that our approach is able to extract a compact pathlet dictionary such that all trajectories can be represented by the concatenations of a few pathlets from the dictionary. We demonstrate the usefulness of the learned pathlet dictionary in route planning.