Continuous clustering of moving objects in spatial networks

Abstract

Spatial-Temporal clustering is one of the most important analysis tasks in spatial databases. Especially, in many real applications, real time data analysis such as clustering moving objects in spatial networks or traffic congestion prediction is more meaningful.Extensive method of clustering moving objects in Euclidean space is more complex and expensive. This paper proposes the scheme of clustering continuously moving objects, analyzes the fixed feature of the road network, proposes a notion of Virtual Clustering Unit (VCU) and improves on the existing algorithm. Performance analysis shows that the new scheme achieves high efficiency and accuracy for continuous clustering of moving objects in road networks. © 2008 Springer-Verlag Berlin Heidelberg.