Kyra Veprek¹, Sina Feldmann¹, William Warren¹; ¹Brown University

The visual control of locomotion has been modeled for individual pedestrian behavior; however, this approach has not been applied to collective human behavior, where spontaneous pattern formation is often observed. We hypothesize that an empirical visual model of human locomotion will reproduce the emergent pattern of lanes and stripes observed in crossing flows, when two groups of pedestrians walk through each other at crosswalks or intersections. Mullick, et al. (2022) manipulated the crossing angle between two groups and found an invariant property: stripe orientation is perpendicular to the mean walking direction (i.e. 90˚ to the bisectrix of the crossing angle). Here we determine the combination of model components required to simulate human-like stripes: (i) steering to a goal (Fajen & Warren, 2003), (ii) collision avoidance with opponents (Bai, 2022; Veprek & Warren, VSS 2023), and (iii) alignment with neighbors (Dachner, et al., 2022), together called the SCruM (Self-organized Collective Motion) model. We performed multi-agent simulations of the data from Mullick et al. (2022), using fixed parameters and initial conditions from the dataset. There were two sets of participants (N=36, 38) with 18 or 19 per group. Crossing angle varied from 60˚ to 180˚ (30˚ intervals), with ~17 trials per condition. The minimal model necessary to reproduce stripe formation consists of the goal and collision avoidance components. Mean stripe orientation did not differ from 90˚ to the bisectrix (BF10 < 0.01, decisive). However, the SD of heading during crossing was significantly larger than the human data (p<0.001), whereas the SD of speed was significantly smaller (p<0.001). Thus, the ratio of heading/speed adjustments is lower than previously found, implying the need to reparameterize model components for walking in groups. In sum, steering to a goal and collision avoidance are sufficient to explain stripe formation in crossing flows, while alignment is unnecessary.

Acknowledgements: NIH R01EY029745