Region-Point Joint Representation for Effective Trajectory Similarity Learning

• URL: http://arxiv.org/abs/2511.13125v1
• Date: Mon, 17 Nov 2025 08:28:18 GMT
• Title: Region-Point Joint Representation for Effective Trajectory Similarity Learning
• Authors: Hao Long, Silin Zhou, Lisi Chen, Shuo Shang,
• Abstract summary: textbfRePo is a novel method that encodes textbfRegion-wise and textbfPoint-wise features to capture both spatial context and fine-grained moving patterns.<n>Experiment results show that RePo achieves an average accuracy improvement of 22.2% over SOTA baselines.
• Score: 25.664203648334563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent learning-based methods have reduced the computational complexity of traditional trajectory similarity computation, but state-of-the-art (SOTA) methods still fail to leverage the comprehensive spectrum of trajectory information for similarity modeling. To tackle this problem, we propose \textbf{RePo}, a novel method that jointly encodes \textbf{Re}gion-wise and \textbf{Po}int-wise features to capture both spatial context and fine-grained moving patterns. For region-wise representation, the GPS trajectories are first mapped to grid sequences, and spatial context are captured by structural features and semantic context enriched by visual features. For point-wise representation, three lightweight expert networks extract local, correlation, and continuous movement patterns from dense GPS sequences. Then, a router network adaptively fuses the learned point-wise features, which are subsequently combined with region-wise features using cross-attention to produce the final trajectory embedding. To train RePo, we adopt a contrastive loss with hard negative samples to provide similarity ranking supervision. Experiment results show that RePo achieves an average accuracy improvement of 22.2\% over SOTA baselines across all evaluation metrics.

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