# TSMini: A Simple Yet Highly Effective Trajectory Similarity Learning Model

arXiv: https://arxiv.org/abs/2502.00285 · HTML: https://mlsanity.com/paper/2502.00285/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 49 references that are in the map.
- First posted on arXiv 1 Feb 2025.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [GLU Variants Improve Transformer](https://mlsanity.com/paper/2002.05202/) (2020)
- [Root Mean Square Layer Normalization](https://mlsanity.com/paper/1910.07467/) (2019)
- [GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints](https://mlsanity.com/paper/2305.13245/) (2023)
- [Large Language Models Are Zero-Shot Time Series Forecasters](https://mlsanity.com/paper/2310.07820/) (2023)
- [Contrastive Trajectory Similarity Learning with Dual-Feature Attention](https://mlsanity.com/paper/2210.05155/) (2022)
- [Deep Spatially and Temporally Aware Similarity Computation for Road Network Constrained Trajectories](https://mlsanity.com/paper/2112.09339/) (2021)
- [Trajectory Similarity Measurement: An Efficiency Perspective](https://mlsanity.com/paper/2311.00960/) (2023)
- [Revisiting CNNs for Trajectory Similarity Learning](https://mlsanity.com/paper/2405.19761/) (2024)
- [Sub-trajectory Similarity Join with Obfuscation](https://mlsanity.com/paper/2106.03355/) (2021)

## Built on by (most cited first, 2 of 2)

- [FlexiReg: Flexible Urban Region Representation Learning](https://mlsanity.com/paper/2503.09128/) (2025)
- [Using Lower-Bound Representations for Trajectory Similarity Learning](https://mlsanity.com/paper/2608.01039/) (2026)

## Abstract (arXiv)

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.