Metraipolis › archive › dc_ce581bc178e1

Coarse-to-Fine Multi-Resolution Diffusion Models for Trajectory Generation in Urban Systems — arXiv:2608.14570

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.14570
License (as archived): CC0-1.0
Archived: 2026-10-18T19:39:41.107Z
SHA-256 of the archived text: c47f60cb382c379abae10c1297b935065c84f84b95838f93700382501a91f089
Stable URL of this page: https://metraipolis.com/documento/dc_ce581bc178e1

How to cite

Coarse-to-Fine Multi-Resolution Diffusion Models for Trajectory Generation in Urban Systems — arXiv:2608.14570 — arXiv (en), https://arxiv.org/abs/2608.14570 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ce581bc178e1 (Metraipolis archive dc_ce581bc178e1, sha256 c47f60cb382c379abae10c1297b935065c84f84b95838f93700382501a91f089)

BibTeX

@misc{dcce581bc178e1,
  title = {Coarse-to-Fine Multi-Resolution Diffusion Models for Trajectory Generation in Urban Systems — arXiv:2608.14570},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.14570},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ce581bc178e1; sha256 c47f60cb382c379abae10c1297b935065c84f84b95838f93700382501a91f089},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.14570. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Understanding human mobility is critical for a wide range of urban applications, including traffic management, epidemic control, and urban planning. However, due to privacy concerns, the availability of large-scale public trajectory data remains limited, posing challenges for downstream mobility analysis. Existing methods for synthetic trajectory generation primarily focus on matching global distribution similarity, while often overlooking mobility patterns across different spatial and temporal resolutions that are essential for practical utility. To address these challenges, we propose a novel multi-resolution diffusion framework, MR-Traj, for large-scale trajectory generation. MR-Traj explicitly models trajectories as compositions of coarse-grained milestones and fine-grained segments, enabling the capture of complex spatial-temporal dependencies at multiple resolutions. Experimental results demonstrate that MR-Traj achieves comparable performance to state-of-the-art methods in terms of global distribution similarity, while consistently outperforming them in modeling fine-resolution mobility patterns and supporting downstream urban mobility tasks. In addition, by introducing stochasticity at multiple resolution levels, MR-Traj generates more diverse trajectories, which empirically reduces trajectory linkage risk under a seed-guided data release setting. Our code is available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-18 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2608.14570 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T19:02:44.016Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).