可编辑地图条件下的轨迹生成用于人类移动性模拟
Editable Map-Conditioned Trajectory Generation for Human Mobility Simulation
AI总结：
本研究提出地图条件下的人类移动性自回归生成方法，通过网格令牌解码器响应编辑地图，实验证明其可行性和地图敏感性。
AI中文摘要：
基础设施干预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条件。我们将此任务定义为地图条件下的人类移动性自回归生成：道路栅格作为条件，解码器以一分钟间隔输出标称31.25米网格单元令牌。网格局部词汇表支持对保留集和局部编辑地图的生成，无需重新训练或更改词汇表。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，并在日本石川县874个网格中的87,400条智能手机轨迹上从头训练；其中219个网格被保留。我们通过比较正确地图和分割内混洗地图的生成与保留的真实轨迹来评估地图敏感性。在110个网格的测试分割上，对于ResNet-50配置，在基于Hausdorff的能量距离下，正确地图生成在60%的网格上比混洗地图生成更接近（p = 0.021），而DTW具有方向性但不确定（57%，p = 0.074）；与真实密度的相关性为0.38（正确地图）对比0.01（混洗地图）。ViT配置显示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改变了生成的延续，而无需重新训练。总之，这些结果支持可编辑地图人类移动性模拟的可行性。
英文摘要：
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.
