基础设施干预措施的地理空间模拟需要能够直接响应编辑后地图的移动性生成器，然而许多数据驱动的生成器并未将地图作为可编辑的条件暴露出来。我们将此任务形式化为基于地图条件的人类移动性自回归生成：道路栅格图作为条件输入解码器，以一分钟为间隔输出标称 31.25 m 网格单元 token。这种网格局部词汇表支持在无需重新训练或更改词汇表的情况下处理留出集和局部编辑后的地图。我们实例化了 ResNet-50 视觉前缀配置和 Vision Transformer (ViT) 交叉注意力配置，并使用来自日本石川县 874 个网格的 87,400 条智能手机衍生轨迹从头开始训练；其中 219 个网格被保留用于测试。通过比较正确地图与组内打乱地图生成的轨迹与留出集真实轨迹来评估地图敏感性。在包含 110 个网格的测试集上，对于 ResNet-50 配置，基于 Hausdorff 距离的能量距离显示，60% 的网格中正确地图生成的轨迹比打乱地图生成的轨迹更接近真实轨迹（p = 0.021），而 DTW 指标虽具方向性但结论不明确（57%，p = 0.074）；正确地图下的密度相关系数为 0.38，而打乱地图下仅为 0.01。ViT 配置表现出较弱的轨迹级敏感性和较小的密度增益。一个说明性的桥梁移除编辑示例表明，该模型能够在无需重新训练的情况下改变生成的后续轨迹。综上所述，这些结果支持了可编辑地图人类移动性模拟的可行性。

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.