Review and Perspective for Distance Based Trajectory Clustering
This is an incremental improvement for geospatial trajectory analysis.
The paper addresses trajectory clustering by reviewing existing distance metrics and introducing a new Symmetrized Segment-Path Distance (SSPD) to overcome limitations, comparing clustering results with hierarchical and affinity propagation methods.
In this paper we tackle the issue of clustering trajectories of geolocalized observations. Using clustering technics based on the choice of a distance between the observations, we first provide a comprehensive review of the different distances used in the literature to compare trajectories. Then based on the limitations of these methods, we introduce a new distance : Symmetrized Segment-Path Distance (SSPD). We finally compare this new distance to the others according to their corresponding clustering results obtained using both hierarchical clustering and affinity propagation methods.