K Nearest Neighbor-Guided Trajectory Similarity Learning

TL;DR

This paper introduces TSMini, a deep learning model that accurately approximates trajectory similarity by modeling multi-granularity patterns and using a k nearest neighbor-based loss, outperforming existing models.

Contribution

TSMini's novel sub-view modeling and k nearest neighbor loss significantly improve trajectory similarity approximation accuracy.

Findings

TSMini outperforms state-of-the-art models by 22% in accuracy.

The model effectively captures multi-granularity trajectory patterns.

The k nearest neighbor-based loss enhances relative similarity ranking.

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 the training data. To fill this gap, we propose TSMini, a highly effective trajectory similarity model with a sub-view modeling mechanism capable of learning multi-granularity trajectory patterns and a k nearest neighbor-based loss that guides TSMini to learn not only absolute similarity values between trajectories but also their relative similarity ranks. Together, these two innovations enable highly accurate trajectory similarity approximation. Experiments show that TSMini can outperform the state-of-the-art models by 22% in accuracy on average when learning trajectory similarity measures.