Traj-Transformer: Diffusion Models with Transformer for GPS Trajectory Generation
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909830749356032 |
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| author | Zhang, Zhiyang Chen, Ningcong Zhang, Xin Li, Yanhua Su, Shen Lu, Hui Luo, Jun |
| author_facet | Zhang, Zhiyang Chen, Ningcong Zhang, Xin Li, Yanhua Su, Shen Lu, Hui Luo, Jun |
| contents | The widespread use of GPS devices has driven advances in spatiotemporal data mining, enabling machine learning models to simulate human decision making and generate realistic trajectories, addressing both data collection costs and privacy concerns. Recent studies have shown the promise of diffusion models for high-quality trajectory generation. However, most existing methods rely on convolution based architectures (e.g. UNet) to predict noise during the diffusion process, which often results in notable deviations and the loss of fine-grained street-level details due to limited model capacity. In this paper, we propose Trajectory Transformer, a novel model that employs a transformer backbone for both conditional information embedding and noise prediction. We explore two GPS coordinate embedding strategies, location embedding and longitude-latitude embedding, and analyze model performance at different scales. Experiments on two real-world datasets demonstrate that Trajectory Transformer significantly enhances generation quality and effectively alleviates the deviation issues observed in prior approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06291 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Traj-Transformer: Diffusion Models with Transformer for GPS Trajectory Generation Zhang, Zhiyang Chen, Ningcong Zhang, Xin Li, Yanhua Su, Shen Lu, Hui Luo, Jun Machine Learning Artificial Intelligence The widespread use of GPS devices has driven advances in spatiotemporal data mining, enabling machine learning models to simulate human decision making and generate realistic trajectories, addressing both data collection costs and privacy concerns. Recent studies have shown the promise of diffusion models for high-quality trajectory generation. However, most existing methods rely on convolution based architectures (e.g. UNet) to predict noise during the diffusion process, which often results in notable deviations and the loss of fine-grained street-level details due to limited model capacity. In this paper, we propose Trajectory Transformer, a novel model that employs a transformer backbone for both conditional information embedding and noise prediction. We explore two GPS coordinate embedding strategies, location embedding and longitude-latitude embedding, and analyze model performance at different scales. Experiments on two real-world datasets demonstrate that Trajectory Transformer significantly enhances generation quality and effectively alleviates the deviation issues observed in prior approaches. |
| title | Traj-Transformer: Diffusion Models with Transformer for GPS Trajectory Generation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2510.06291 |