Profiling Moving Objects by Dividing and Clustering Trajectories Spatiotemporally

An object can move with various speeds and arbitrarily changing directions. Given a bounded area where a set of objects moving around, there are some typical moving styles of the objects at different local regions due to the geography nature or other spatiotemporal conditions. Not only the paths that the objects move along, we also want to know how different groups of objects move with various speeds. Therefore, given a set of collected trajectories spreading in a bounded area, we are interested in discovering the typical moving styles in different regions of all the monitored moving objects. These regional typical moving styles are regarded as the profile of the monitored moving objects, which may help reflect the geoinformation of the observed area and the moving behaviors of the observed moving objects. In this paper, we present DivCluST, an approach to finding regional typical moving styles by dividing and clustering the trajectories in consideration of both the spatial and temporal constraints. Different from the existing works that consider only the spatial properties or just the interesting regions of trajectories, DivCluST focuses more on typical movements in local regions of a bounded area and takes the temporal information into account when designing the criteria for trajectory dividing and the distance measurement for adaptive $(k)$-means clustering. Extensive experiments on three types of real data sets with specially designed visualization are presented to show the effectiveness of DivCluST.