Road-Network Aware Trajectory Clustering: Integrating Locality, Flow, and Density
Binh Han, Ling Liu, Edward R. Omiecinski · IEEE Transactions on Mobile Computing · 2013
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. We propose NEAT-a road-network aware approach for fast and effective clustering of trajectories of mobile objects traveling in road networks. Our approach carefully considers the traffic locality characterized by the physical constraints of the road network, the traffic flow among consecutive road segments, and the flow-based density to organize trajectories into spatial clusters in a comprehensive three-phase clustering framework. NEAT discovers spatial clusters as groups of sub-trajectories which describe both dense and highly continuous flows of mobile objects. We perform extensive experiments with mobility traces generated using different scales of real road networks. Experimental results demonstrate the flexibility of the NEAT system and show that NEAT is highly accurate and runs orders of magnitude faster than existing density-based trajectory clustering approaches.