用神经网络模拟人群行为，实现可控且高精度的虚拟实验。
Understanding complex crowd dynamics with generative neural simulators
- 基于大规模数据训练神经人群模拟器，实现实验室级控制
- 重现双人避让现象，发现多人交互具视觉引导与拓扑特性
- 适合城市规划、安全管理和计算社会科学研究者
理解行人客流动态是设计高效城市基础设施和保障人群安全的关键挑战。现有方法依赖小规模实验室实验或大规模现实观测，但分别缺乏统计分辨率与参数可控性，难以揭示客流复杂随机动力学背后的物理关系。本文提出一种新研究范式，兼具实验室级可控性与真实数据集的统计分辨率。我们利用基于大数据训练并验证于关键统计特征的神经人群模拟器（NeCS），无需针对特定场景训练即可开展有效的虚拟实验。不仅复现了已知的双人避让结果，还揭示了多人交互中存在视觉引导与拓扑结构特性。这表明基于神经模拟的虚拟实验可推动数据驱动的科学发现。
原文摘要 · Abstract (English)
Understanding the dynamics of pedestrian crowds is an outstanding challenge crucial for designing efficient urban infrastructure and ensuring safe crowd management. To this end, both small-scale laboratory and large-scale real-world measurements have been used. However, these approaches respectively lack statistical resolution and parametric controllability, both essential to discovering physical relationships underlying the complex stochastic dynamics of crowds. Here, we establish an investigation paradigm that offers laboratory-like controllability, while ensuring the statistical resolution of large-scale real-world datasets. Using our data-driven Neural Crowd Simulator (NeCS), which we train on large-scale data and validate against key statistical features of crowd dynamics, we show that we can perform effective surrogate crowd dynamics experiments without training on specific scenarios. We not only reproduce known experimental results on pairwise avoidance, but also uncover the vision-guided and topological nature of N-body interactions. These findings show how virtual experiments based on neural simulation enable data-driven scientific discovery.
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