Title :
Online Discovery of Gathering Patterns over Trajectories
Author :
Kai Zheng ; Yu Zheng ; Yuan, Nicholas Jing ; Shuo Shang ; Xiaofang Zhou
Author_Institution :
Sch. of Inf. Technol. & Electr. Eng., Univ. of Queensland, Brisbane, QLD, Australia
Abstract :
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 behaviors 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 modeling 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. In this work, we first develop a set of novel techniques to tackle the challenge of efficient discovery of gathering patterns on archived trajectory dataset. Afterwards, since trajectory databases are inherently dynamic in many real-world scenarios such as traffic monitoring, fleet management and battlefield surveillance, we further propose an online discovery solution by applying a series of optimization schemes, which can keep track of gathering patterns while new trajectory data arrive. 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.
Keywords :
data mining; battlefield surveillance; fleet management; gathering concept; location-acquisition technologies; movement behaviors; optimization scheme; pattern discovery; pattern gathering; taxicab trajectory dataset; traffic monitoring; trajectory data; trajectory dataset; trajectory pattern modeling; Clustering algorithms; Data mining; Databases; Educational institutions; Monitoring; Shape; Trajectory; Database Applications; Spatial databases and GIS; Trajectory database; gathering pattern; pattern mining;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
DOI :
10.1109/TKDE.2013.160