Abstract
AbstractSearching similar trajectories in real time has been a challenging task in a large variety of location-aware applications. This paper addresses its two key issues, i.e. evaluating the similarity between two trajectories reasonably and effectively, and providing efficient algorithms to support queries in real time. Firstly, a novel similarity measurement, called Global Temporal Similarity (GTS), is suggested, which is perturbation-free and effective since it takes into account both the evolution of the similarity over time and the spatial movements. Secondly, a new index structure with linear updated time, called Real Time Similar Trajectory Searching-tree (RTSTS-tree), is proposed to support the search of similar trajectories. Besides, to support k Nearest Neighbor (kNN) query of trajectories and Top k Similar Pairs query, two algorithms are proposed based on GTS and RTSTS-tree and are capable of searching similar trajectories by object and by location with the time complexity of O(n) and O(n 2) respectively. Finally, the results of the extensive experiments conducted on real and synthetic data set validate the effectiveness and the efficiency of the proposed similarity measurement, index structure and query algorithms.KeywordsTrajectory similaritytrajectory indextrajectory querykNN queryTop k query
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