Online Clustering of Trajectories in Road Networks

The ubiquity of GPS-enabled smartphones and automotive navigation systems allows to monitor and collect massive streams of trajectory data in real-Time. This enables real-Time analyses on mobility data in urban settings, which in turn have the potential to substantially improve traffic conditions, analyze congested areas, detect events in (quasi) real-Time, and so on. While many existing approaches characterize past movements of moving objects from historical trajectory data, or address the problem of finding out clusters of moving objects from data streams, such approaches fail to capture how movement behaviors unravel over time-for instance, they fail to capture typically trafficked routes or traffic jams. In this work we propose NET-CUTiS, a novel approach that addresses the problem of discovering and monitor the evolution of clusters of trajectories over road networks from trajectory data streams. We conduct several experiments that demonstrate the validity of our proposal in terms of clustering quality and run-Time performance.