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Trajectory Similarity Hash Learning With Spatio-Temporal GRU
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DOI：10.1109/tbdata.2026.3673416.png)
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Abstract
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Trajectory similarity computation plays a critical role in a wide range of trajectory-related applications, including transportation optimization and behavior study. Most studies aim at learning discriminative real-valued trajectory representations. However, these methods struggle to scale to large datasets due to their linear time complexity. To address this problem, only one hypergraph hash learning approach (HHL-Traj) has been proposed to realize efficient trajectory similarity computation by calculating Hamming distances among the trajectory hash codes. Nevertheless, it fails to effectively integrate the spatial and temporal information of trajectory data with semantic relevance. In this paper, we present a novel Trajectory Similarity Hash Learning method with Spatio-Temporal GRU (TrajH-ST), which fuses the spatial and temporal features through reset and update gates within the network architecture. Additionally, we design an alternating sampling strategy to generate two sub-trajectories for contrastive learning, which enhances both generalization and robustness compared to nonuniform sampling techniques. To optimize the proposed end-to-end model, we develop an objective function that incorporates InfoNCE loss, alignment loss, and quantization loss. Extensive experiments on two widely-used trajectory datasets demonstrate that the proposed model consistently outperforms state-of-the-art baselines, achieving accuracy improvements of up to 5.58% and 4.33% with real-valued and binary features, respectively.
Keywords:
Trajectory similarity computation
hash learning
spatio-temporal GRU
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