NEAT: Road Network Aware Trajectory Clustering

—Mining trajectory data has been gaining significant interest in recent years. However, existing approaches to trajectory clustering are mainly based on density and Euclidean distance measures. We argue that when the utility of spatial clustering of mobile object trajectories is targeted at road network aware location based applications, density and Euclidean distance are no longer the effective measures. This is because traffic flows in a road network and the flow-based density characterization become important factors for finding interesting trajectory clusters of mobile objects travelling in road networks. In this paper, we propose NEAT−a road network aware approach for fast and effective clustering of spatial trajectories of mobile objects travelling in road networks. Our method takes into account the physical constraints of the road network, the network proximity and the traffic flows among consecutive road segments to organize trajectories into spatial clusters. The cl...

• Type: Journal
• Year: 2012
• Where: ICDCS
• Authors: Binh Han, Ling Liu, Edward Omiecinski
• Keywords: Distributed And Parallel Computing, ICDCS 2012, Network Proximity, Road Segments, Spatial Clusters