发表机构
Eindhoven University of Technology（埃因霍温理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对等待行人空间分布，提出在Gibbs点过程模型中检测社会群体并替换为宏观行人的方法，以改进最近邻统计量，虽未完全解决但为后续研究开辟了方向。
AI 中文摘要
等待行人的空间分布有两个主要驱动因素：环境偏好和与其他行人的互动。空间点过程自然地同时捕捉空间异质性和排斥性互动。在先前的工作中（Sickert Karam等人，arXiv:2606.14532，2026），我们提出了一个具有非均匀强度和修正的Diggle-Gates-Stibbard交互函数的Gibbs模型，该模型再现了火车站等待行人的重复模式中的许多现象。其单一交互函数充当有效交互，平均了行为机制，如社会群体内部互动和陌生人之间的互动。在本文中，我们展示了群体如何影响基于距离的汇总统计量，并迈出了考虑它们的第一步。我们从成对距离和接触持续时间中检测群体，将每个群体替换为其平均位置处的“宏观行人”，并重新拟合模型。这产生了比原始模型更大的交互范围和更好的最近邻统计量一致性，尽管这两种拟合涉及不同的数据集，且基于质心的距离可能高估排斥性。因此，这一首次尝试解决了一些差异，但尚未完全充分，为进一步研究开辟了途径。
英文摘要
The spatial distribution of waiting pedestrians has two primary drivers: environmental preference and interaction with other pedestrians. Spatial point processes naturally capture both spatial heterogeneity and repulsive interaction. In previous work (Sickert Karam et al., arXiv:2606.14532, 2026), we proposed a Gibbs model with inhomogeneous intensity and a modified Diggle-Gates-Stibbard interaction function, which reproduces many phenomena in replicated patterns of pedestrians waiting at a train station. Its single interaction function acts as an effective interaction, averaging over behavioral regimes such as interactions within social groups and among strangers. In this article, we show how groups affect distance-based summary statistics and take first steps towards accounting for them. We detect groups from pairwise distances and contact durations, replace each group by a ``macro-pedestrian'' at its average position, and refit the model. This yields a larger interaction range and better agreement in nearest-neighbor statistics than the original model, although the two fits concern different datasets and centroid-based distances likely overstate repulsion. This first attempt thus resolves some discrepancies but falls short of a fully adequate solution, opening avenues for further research.
Comments13 pages, 7 figures
