Discovering frequent mobility patterns on moving object data

We consider the problem of efficiently discovering and detecting frequent mobility patterns on moving object data. Our proposed approach is key for mobility applications, such as applications that need to discover and explain movement patterns of a set of moving objects (e.g. traffic management, birds migration, disease spreading). In this sense, we developed a method that performs density based clustering on trajectory data at regular time intervals, then we analyze clusters evolution, which is characterized by appear, disappear, expand, shrink, split, merge and survive. To solve our problem, a graph-based representation called Graph Evolution Cluster over Time (Δevol) is described and an algorithm to generate the graph is also presented. Finally, we map our problem to the problem of discovering frequent graph paths on Δevol. Therefore, the frequent graph paths are the frequent sequence of evolution patterns that occurs in the dataset. We discuss a preliminary solution to this problem and present some experimental results. The results suggest that evolution patterns and their frequency can be effectively obtained through the proposed Δevol obtained from moving object data.