The volume of trajectory data has become tremendously large in recent years. How to effectively and efficiently search similar trajectories has become an important task. Firstly, to measure the similarity between a trajectory and a query, literature works compute spatial similarity and temporal similarity independently, and next sum the two weighted similarities. Thus, two trajectories with high spatial similarity and low temporal similarity will have the same overall similarity with another two trajectories with low spatial similarity and high temporal similarity. To overcome this issue, we propose to measure the similarity by synchronously matching the spatial distance against temporal distance. Secondly, given this new similarity measurement, to overcome the challenge of searching top-k similar trajectories over a huge trajectory database with non-trivial number of query points, we propose to efficiently answer the top-k similarity search by following two techniques: trajectory database grid indexing and query partitioning. The performance of our proposed algorithms is studied in extensive experiments based on two real data sets.

近年来，轨迹数据的数量变得非常大。如何有效，高效地搜索相似的轨迹已成为重要的任务。首先，为了测量轨迹和查询之间的相似度，文献工作独立地计算空间相似度和时间相似度，然后将两个加权相似度求和。因此，具有高空间相似性和低时间相似性的两个轨迹将具有与另外两个具有低空间相似性和高时间相似性的轨迹相同的整体相似性。为了克服这个问题，我们建议通过将空间距离与时间距离同步匹配来测量相似度。其次，鉴于这种新的相似性度量，要克服搜索top- k的挑战在具有非平凡查询点数量的巨大轨迹数据库上的相似轨迹，我们建议通过以下两种技术有效地回答top- k相似性搜索：轨迹数据库网格索引和查询分区。我们的算法的性能在基于两个真实数据集的大量实验中得到了研究。