{
  "abstract": "Geospatial simulation of infrastructure interventions requires mobility generators that respond directly to edited maps, yet many data-driven generators do not expose the map as an editable condition. We formulate this task as map-conditioned autoregressive generation of human mobility: a road raster conditions a decoder that emits nominal 31.25 m mesh-cell tokens at one-minute intervals. The mesh-local vocabulary supports held-out and locally edited maps without retraining or vocabulary changes. We instantiate a ResNet-50 visual-prefix configuration and a Vision Transformer (ViT) cross-attention configuration, trained from scratch on 87,400 smartphone-derived trajectories from 874 meshes in Ishikawa Prefecture, Japan; 219 meshes are held out. We evaluate map sensitivity by comparing correct-map and within-split shuffled-map generations with held-out real trajectories. On the 110-mesh test split, for the ResNet-50 configuration, correct-map generations are closer than shuffled-map generations on 60% of meshes under Hausdorff-based energy distance (p = 0.021), while DTW is directional but inconclusive (57%, p = 0.074); correlation with real density is 0.38 with the correct map versus 0.01 with shuffled maps. The ViT configuration shows weaker trajectory-level sensitivity and smaller density gains. An illustrative bridge-removal edit changes generated continuations without retraining. Together, these results support the feasibility of editable-map human-mobility simulation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.32360",
  "author_total": 4,
  "authors": [
    {
      "name": "Takayuki Mizuno"
    },
    {
      "name": "Shouji Fujimoto"
    },
    {
      "name": "Mikito Hiruki"
    },
    {
      "name": "Atushi Ishikawa"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c9f3azbegx1fz.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.32360",
  "fulltext_url": null,
  "id": "c9f3azbegx1fz",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.32360",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CY"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.soc-ph"
    ],
    [
      "openalex",
      "3313|Human Mobility and Location-Based Analysis"
    ]
  ],
  "openalex": "W7214764022",
  "pdf_url": "https://arxiv.org/pdf/2609.32360",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/c9f3azbegx1fz.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:12Z",
      "native_id": "2609.32360",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.32360"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.soc-ph",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "Editable Map-Conditioned Trajectory Generation for Human Mobility Simulation",
  "title_text": "Editable Map-Conditioned Trajectory Generation for Human Mobility Simulation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:12Z",
  "url": "https://research.draconitic.com/paper/c9f3azbegx1fz",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1145/3849737.3849831",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}