基于环的空间Transformer：学习建筑分布与行人流量之间的非线性空间相互作用
Ring-based Spatial Transformer: Learning Non-linear Spatial Interactions between Building Distribution and Pedestrian Flow
AI总结：
该研究提出基于环的SpatialTransformer模型，利用东京100个火车站的环缓冲区数据学习建筑分布与行人流量的非线性空间相互作用，其预测准确率优于GWR，挑战了紧凑城市假设，为城市规划提供了新视角。
AI中文摘要：
本研究提出了一种基于环的SpatialTransformer（空间Transformer），用于学习距火车站不同距离处的建筑用途如何相互作用以产生行人流量。研究在东京随机选取的100个火车站周围，定义了间隔为100米、最远至800米的同心环缓冲区，将每个环视为一个空间token（标记）。该模型应用自注意力机制直接从数据中学习区域间的相互作用，无需预先设定结构假设。研究采用GPS-derived（基于GPS的）步行出行次数作为目标变量，并将Geographically Weighted Regression（地理加权回归，GWR）作为基准模型。在30次独立试验中，SpatialTransformer的预测准确率始终优于GWR。SHAP分析显示，中到远距离区域的特征对行人流量预测起主导作用，而0-100米区域的特征贡献极小。注意力矩阵表明，每个距离区域最关注空间上遥远的区域，这说明行人流量受整个集水区的结构相互作用调控，而非仅由单个区域单独决定。这些发现对紧凑城市假设（即车站周边开发可最大化行人流量）提出了挑战，建议在城市规划实践中应更全面地考虑整个步行可达集水区的土地利用分布。
英文摘要：
This study proposes a ring-based SpatialTransformer to learn how building uses at different distances from a railway station interact to generate pedestrian flow. Concentric ring buffers at 100-meter intervals up to 800 meters were defined around 100 randomly selected stations in Tokyo, treating each ring as a spatial token. Self-Attention was applied to learn inter-zone interactions directly from data, without prior structural assumptions. GPS-derived walking trip counts served as the target variable and Geographically Weighted Regression as the baseline. Across 30 independent trials, the SpatialTransformer consistently outperformed GWR in predictive accuracy. SHAP analysis revealed that mid-to-outer distance zone features dominate pedestrian flow prediction, while features from the 0-100m zone contributed little. The attention matrix showed that each distance zone attends most strongly to spatially distant zones, demonstrating that pedestrian flow is regulated by structural interactions across the entire catchment area rather than by any single zone in isolation. These findings challenge the compact city assumption that station-proximate development maximizes pedestrian flow, and suggest that land use distribution across the full walkable catchment area deserves greater consideration in urban planning practice.
