Semantic trajectories modeling and analysis
ACM Computing Surveys (CSUR)
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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 clusters discovered by NEAT are groups of sub-trajectories which describe both dense and highly continuous traffic flows of mobile objects. We perform extensive experiments with mobility traces generated using different scales of real road network maps. Our experimental results demonstrate that the NEAT approach is highly accurate and runs orders of magnitude faster than existing density-based trajectory clustering approaches.