Spatio-temporal Similarity Measure for Trajectories on Road Networks

Trajectories play an important role in analyzing the behavior of moving objects. Many researches have been conducted that retrieved similar trajectories of moving objects in Euclidean space rather than in road network space. However, in real applications, most moving objects are located in road network space. In this paper, we investigate the properties of similar trajectories in road network space and propose a spatio-temporal representation scheme for modeling the trajectories of moving objects. Our spatio-temporal representation scheme effectively converts trajectory from the road network space to the Euclidean space. For measuring similarity between two trajectories, we propose a new POI-distance algorithm which enhances the existing distance algorithm by reducing the insignificant nodes of a trajectory. Theory and experimental results show that this method provide not only a practical method for searching for similar trajectories but also a clustering method for trajectories.