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This research introduces TSMini, a deep learning model designed to improve the accuracy of trajectory similarity approximation in spatio-temporal data mining. By integrating sub-view modeling for multi-granularity patterns and a k-nearest neighbor loss function, it outperforms existing methods by 15% on average while maintaining fast inference speeds. The work addresses specific challenges in modeling trajectory granularity and extracting relative similarity ranks from training data.
TSMini: A Simple Yet Highly Effective Trajectory Similarity Learning Model
arXiv:2502.00285v3 Announce Type: replace Abstract: Trajectory similarity is fundamental to many spatio-temporal data mining applications. Recent studies propose deep learning models to approximate conventional trajectory similarity measures, exploiting their fast inference time once trained. Although efficient inference has been reported, challenges remain in similarity approximation accuracy due to difficulties in trajectory granularity modeling and in exploiting similarity signals in training data. To fill this gap, we propose TSMini, a highly effective trajectory similarity model with a sub-view modeling mechanism and a k nearest neighbor-based loss. The former enables learning multi-granularity trajectory patterns, while the latter guides TSMini to learn not only absolute similarity values between trajectories but also their relative similarity ranks. Together, these innovations enable highly accurate trajectory similarity approximation. Experiments show that TSMini outperforms the state-of-the-art models by 15% on average when learning widely used trajectory similarity measures.
Abstract:Trajectory similarity is fundamental to many spatio-temporal data mining applications. Recent studies propose deep learning models to approximate conventional trajectory similarity measures, exploiting their fast inference time once trained. Although efficient inference has been reported, challenges remain in similarity approximation accuracy due to difficulties in trajectory granularity modeling and in exploiting similarity signals in training data. To fill this gap, we propose TSMini, a highly effective trajectory similarity model with a sub-view modeling mechanism and a k nearest neighbor-based loss. The former enables learning multi-granularity trajectory patterns, while the latter guides TSMini to learn not only absolute similarity values between trajectories but also their relative similarity ranks. Together, these innovations enable highly accurate trajectory similarity approximation. Experiments show that TSMini outperforms the state-of-the-art models by 15% on average when learning widely used trajectory similarity measures.
Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Databases (cs.DB)
arXiv:2502.00285 [cs.LG]
(or arXiv:2502.00285v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2502.00285 Focus to learn more arXiv-issued DOI via DataCite
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