CPM: Mining Converging Patterns from Moving Object Trajectories in Road Networks

Abstract

Group pattern mining from spatio-temporal trajectories of moving objects have gained significant attentions due to the prevalence of location-acquisition devices and tracking technologies. In this work, we propose a new group pattern, named converging, which is a group of moving objects that converge from different directions for a certain time period. Examples of convergings may include traffic jams, troop assembly, serious stampedes, and other public congregations. As a proof-of-concept, we implemented a visual analytic system CPM based on road-network constrained trajectories to detect converging events in road networks. A user-friendly interface is designed to help users gain insights into converging events from spatial and temporal aspects. Finally, we demonstrate the effectiveness and efficiency of our system by using a real dataset.