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arXiv · 2507.05542

GTRSS: Graph-based Top-$k$ Representative Similar Subtrajectory Query

Mingchang Ge·Liping Wang·Xuemin Lin·Yuang Zhang·Kunming Wang
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Abstract

Trajectory mining has attracted significant attention. This paper addresses the Top-k Representative Similar Subtrajectory Query (TRSSQ) problem, which aims to find the k most representative subtrajectories similar to a query. Existing methods rely on costly filtering-validation frameworks, resulting in slow response times. Addressing this, we propose GTRSS, a novel Graph-based Top-k Representative Similar Subtrajectory Query framework. During the offline phase, GTRSS builds a dual-layer graph index that clusters trajectories containing similar representative subtrajectories. In the online phase, it efficiently retrieves results by navigating the graph toward query-relevant clusters, bypassing full-dataset scanning and heavy computation. To support this, we introduce the Data Trajectory Similarity Metric (DTSM) to measure the most similar subtrajectory pair. We further combine R-tree and grid filtering with DTSM pruning rules to speed up index building. To the best of our knowledge, GTRSS is the first graph-based solution for top-k subtrajectory search. Experiments on real datasets demonstrate that GTRSS significantly enhances both efficiency and accuracy, achieving a retrieval accuracy of over 90 percent and up to two orders of magnitude speedup in query performance.

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Mingchang Ge, Liping Wang, Xuemin Lin, Yuang Zhang, Kunming Wang. 2025-07-07. GTRSS: Graph-based Top-$k$ Representative Similar Subtrajectory Query. https://arxiv.org/abs/2507.05542

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