Trajectory-Embedded Matryoshka Representation Learning for Enhanced Similarity Analysis

This paper introduces Trajectory-Embedded Matryoshka Representation Learning (TE-MRL). This novel framework synergies the capabilities of trajectory representation learning with the adaptability and efficiency of Matryoshka Representation Learning (MRL). TE-MRL is engineered to generate adaptive, multi-granular embeddings that efficiently capture the spatial-temporal dynamics inherent in trajectory data. We evaluate TE-MRL on the Porto dataset, focusing on trajectory similarity and k-nearest trajectory similarity tasks. Our findings demonstrate that TE-MRL preserves critical features such as travel semantics and temporal regularities while it can significantly reduce computational time and memory footprint. The proposed approach matches existing methods' accuracy and efficiency but demonstrates robust adaptability under varying computational constraints. Furthermore, we proposed a two-stage retrieval pipeline to enhance computational time while maintaining the same precision. We reduced the computation time by 8× while maintaining state-of-the-art precision. The effectiveness of TE-MRL in handling the complexity of the Porto dataset underlines its potential for broader applications in urban computing and mobility analytics.