How subgroups affect the power law governing pedestrian avoidance interactions

Pedestrian motion, driven by continuous interactions that govern individual navigation and collective behavior, is crucial for understanding and managing complex crowd dynamics in urban public spaces. However, most existing studies treat pedestrians as isolated individuals, overlooking how subgroup organization affects collision avoidance and interaction laws. To address this, we develop a subgroup-aware framework to capture the most imminent subgroup-involved encounter by minimizing pairwise Time-To-Collision (TTC) within subgroups, thereby filtering redundant interactions and prioritizing behavioral realism. This data-driven analysis, applied to five public pedestrian datasets, examines the pair distribution function and interaction energy, showing that subgroup consideration yields a systematically smaller power-law exponent than the independent-individual assumption, revealing a weaker but more focused interaction decay. Moreover, the scaling exponent increases with subgroup size, indicating amplified repulsive forces and sharper avoidance maneuvers when facing larger subgroups. As an empirical feature-discovery study, these findings provide a more realistic and behaviorally grounded basis for modeling pedestrian dynamics, offering valuable implications for crowd simulation, public-space design, and autonomous systems.