Abstract
Understanding pedestrian crowd dynamics is a fundamental challenge in active matter physics and crucial for efficient urban infrastructure design. Complexity emerges from social interactions, which are often qualitatively modeled as distance-based additive forces. Endeavors towards quantitative characterizations have been limited by a trade-off between parametric control in laboratory studies and statistical resolution of large-scale real-world measurements. To bridge this gap, we propose a virtual surrogate experimentation paradigm that combines laboratory-like control with real-world statistical resolution. Our approach hinges on a generative simulation model based on graph neural networks, which we train on real-world pedestrian tracking data and validate against key statistical properties of crowd dynamics. Our surrogate experiments not only reproduce known experimental results on collision avoidance, but also reveal new insights into N-body interactions in crowds, which have remained poorly understood. We find that these interactions are topological, with individuals reacting to a limited number of neighbors within a narrow field of view. Our study exemplifies how data-driven approaches can uncover fundamental interaction structures in social systems, even when only uncontrolled measurements are available. This approach opens new avenues for scientific discovery in complex systems where laboratory studies are prohibitive, from crowd dynamics and animal behavior to opinion formation.