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ScaleTraj: Scale-Decomposed Human Trajectory Generation — arXiv:2611.04097

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.04097
License (as archived): CC0-1.0
Archived: 2026-11-04T21:51:28.850Z
SHA-256 of the archived text: b4bb9c41e4b12c696fc46e3f30e5b032ecd1cea913093dba9acb1fc924d902ad
Stable URL of this page: https://metraipolis.com/documento/dc_0e92c5bc9155

How to cite

ScaleTraj: Scale-Decomposed Human Trajectory Generation — arXiv:2611.04097 — arXiv (en), https://arxiv.org/abs/2611.04097 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_0e92c5bc9155 (Metraipolis archive dc_0e92c5bc9155, sha256 b4bb9c41e4b12c696fc46e3f30e5b032ecd1cea913093dba9acb1fc924d902ad)

BibTeX

@misc{dc0e92c5bc9155,
  title = {ScaleTraj: Scale-Decomposed Human Trajectory Generation — arXiv:2611.04097},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.04097},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_0e92c5bc9155; sha256 b4bb9c41e4b12c696fc46e3f30e5b032ecd1cea913093dba9acb1fc924d902ad},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.04097. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Individual-level trajectories are essential for applications that rely on user-level mobility behavior. Existing mobility-generation approaches face a fundamental tension between individual-level and population-level statistical fidelity: top-down models reproduce aggregate mobility statistics but struggle to guarantee realistic individual trajectories, while bottom-up generators synthesize individual trajectories yet often fail to preserve key population-level mobility regularities after aggregation. In particular, many bottom-up generated populations fail to reproduce the displacement distribution, whose heavy tail is well summarized by a truncated power law. We formalize this phenomenon as the \textbf{Population-Level Displacement Gap}. To investigate its prevalence, we conduct a systematic evaluation of representative model-driven and data-driven trajectory generators. Our results provide evidence that the Population-Level Displacement Gap is prevalent across diverse generation paradigms. To diagnose this gap, we introduce the \textbf{Scale Fidelity Profile (SFP)}, a scale-decomposed metric that quantifies discrepancies in trip allocations across spatial scales. SFP indicates that population-level displacement mismatches co-occur with systematic errors in how trips are allocated across spatial scales. Motivated by this observation, we develop \textbf{ScaleTraj}, an autoregressive Transformer that decomposes location prediction into a Scale Selection Head that decides the movement scale and a Scale-Aware Cell Selection Head that predicts the destination conditioned on the selected scale. Experiments on two datasets show that ScaleTraj reduces the Population-Level Displacement Gap versus model-driven and data-driven baselines, while preserving fidelity. Our code is available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2611.04097 — 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 2026-11-04T21:38:49.699Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).