Kai Zheng, Yu Zheng, Nicholas Jing Yuan, and Shuo Shang
The increasing pervasiveness of location-acquisition technologies has enabled collection of huge amount of trajectories for almost any kind of moving objects. Discovering useful patterns from their movement behaviours can convey valuable knowledge to a variety of critical applications. In this light, we propose a novel concept, called gathering, which is a trajectory pattern modelling various group incidents such as celebrations, parades, protests, traffic jams and so on. A key observation is that these incidents typically involve large congregations of individuals, which form durable and stable areas with high density. Since the process of discovering gathering patterns over large-scale trajectory databases can be quite lengthy, we further develop a set of well thought out techniques to improve the performance. These techniques, including effective indexing structures, fast pattern detection algorithms implemented with bit vectors, and incremental algorithms for handling new trajectory arrivals, collectively constitute an efficient solution for this challenging task. Finally, the effectiveness of the proposed concepts and the efficiency of the approaches are validated by extensive experiments based on a real taxicab trajectory dataset.
|Published in||IEEE International Conference on Data Engineering (ICDE 2013)|
Kai Zheng, Yu Zheng, Nicholas Jing Yuan, Shuo Shang, and Xiaofang Zhou. Online Discovery of Gathering Patterns over Trajectories, IEEE Transaction on Knowledge Discovery and Data Engineering, IEEE Transactions on Knowledge and Data Engineering (TKDE), 2014.