Active flow of low density pedestrian crowds

The dynamics of pedestrian crowds share deep connections with the statistical physics of active matter.
Pedestrians move following own will and objectives: this amounts in huge variability in the motion, observable even in diluted conditions. Despite such individual unpredictability, ensemble-level universal physical features emerge and encompass common and rare fluctuations of both the solo and the interacting dynamics. Reaching a quantitative understanding of these features is a major scientific challenge with deep societal impact, e.g. in the design of civil infrastructures or of crowd management measures.

We investigate from observational experiments held in real-life settings (stations, festivals, museums) and in diluted conditions statistical features of pedestrian motion. We leverage on datasets including millions of trajectories acquired with and without external influencing stimuli (i.e., crowd control measures, like signage or visual cues), via home-made high-fidelity tracking systems. On this basis, we quantify the PDFs of individual velocity, position, body rotation and mutual-contact-avoidance "social" forces - possibly in dependence on stimuli. We propose an active-Brownian particle model of the dynamics based on Langevin-like equations statistically quantitative.