Movement similarity assessment using symbolic representation of trajectories

Abstract

This paper describes a novel approach for finding similar trajectories, using trajectory segmentation based on movement parameters such as speed, acceleration, or direction. First, a segmentation technique is applied to decompose trajectories into a set of segments with homogeneous characteristics with respect to a particular movement parameter. Each segment is assigned to a movement parameter class, representing the behavior of the movement parameter. Accordingly, the segmentation procedure transforms a trajectory to a sequence of class labels, that is, a symbolic representation. A modified version of edit distance, called Normalized Weighted Edit Distance (NWED) is introduced as a similarity measure between different sequences. As an application, we demonstrate how the method can be employed to cluster trajectories. The performance of the approach is assessed in two case studies using real movement datasets from two different application domains, namely, North Atlantic Hurricane trajectories and GPS tracks of couriers in London. Three different experiments have been conducted that respond to different facets of the proposed techniques, and that compare our NWED measure to a related method.