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Discovering Evolving Moving Object Groups from Massive-Scale Trajectory Streams

The increasing pervasiveness of object tracking technologies leads to huge volumes of spatio-temporal data collected in the form of trajectory streams. The discovery of useful group patterns from moving objects' movement behaviors in trajectory streams is critical for real time applications ranging from transportation management to military surveillance. In this work we propose a novel type of group pattern, called evolving group, which models the unusual group events of moving objects that travel together within density connected clusters in evolving streaming trajectories. Our theoretical analysis and empirical study on the Beijing Taxi data demonstrate its effectiveness in capturing development, evolution and trend of group events of moving objects in streaming context. Furthermore, we propose a discovery framework that efficiently supports online detection of evolving groups over massive-scale trajectory streams using sliding window. It contains three phases along with a set of novel optimization techniques designed to minimize the computation costs. Our comprehensive empirical study demonstrates that our discovery framework is effective and efficient on real-world high volume trajectory streams.