用超图捕捉城市出行中多地点协同移动，揭示传统模型忽略的复杂互动。
Identifying and Characterising Higher Order Interactions in Mobility Networks Using Hypergraphs
- 通过时间窗口提取轨迹数据中的群体访问模式，构建动态超图。
- 能检测极端天气等外部冲击下的出行模式变化，识别连通性与兴趣点数量的关系。
- 适合城市规划、公共健康和灾害韧性研究者使用。
理解人类出行行为对城市规划、公共卫生等领域至关重要。传统出行模型如流量网络和共现矩阵仅能刻画地点间的成对互动，忽视了多个地点之间的高阶关系（即多地点间的出行流）。为此，我们提出共访问超图，利用时间观测窗口从个体出行轨迹数据中提取地点间的群体互动。通过频繁模式挖掘，该方法构建出能反映不同空间与时间尺度下动态出行行为的超图。我们在公开的出行数据集上验证了该方法的有效性，结果表明其可有效分析城市级出行模式，检测极端天气等外部事件引发的出行变化，并揭示某地点连通性（度）与其内部兴趣点数量之间的关联。实验表明，基于超图的出行分析框架是一项有价值的工具，具有在公共卫生、灾害韧性及城市规划等领域的应用潜力。
原文摘要 · Abstract (English)
Understanding human mobility is essential for applications ranging from urban planning to public health. Traditional mobility models such as flow networks and colocation matrices capture only pairwise interactions between discrete locations, overlooking higher-order relationships among locations (i.e., mobility flow among two or more locations). To address this, we propose co-visitation hypergraphs, a model that leverages temporal observation windows to extract group interactions between locations from individual mobility trajectory data. Using frequent pattern mining, our approach constructs hypergraphs that capture dynamic mobility behaviors across different spatial and temporal scales. We validate our method on a publicly available mobility dataset and demonstrate its effectiveness in analyzing city-scale mobility patterns, detecting shifts during external disruptions such as extreme weather events, and examining how a location's connectivity (degree) relates to the number of points of interest (POIs) within it. Our results demonstrate that our hypergraph-based mobility analysis framework is a valuable tool with potential applications in diverse fields such as public health, disaster resilience, and urban planning.
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