Local interactions underlying collective motion in human crowds.

It is commonly believed that global patterns of motion in flocks, schools and crowds emerge from local interactions between individuals, through a process of self-organization. The key to explaining such collective behaviour thus lies in deciphering these local interactions. We take an experiment-driven approach to modelling collective motion in human crowds. Previously, we observed that a pedestrian aligns their velocity vector (speed and heading direction) with that of a neighbour.Herewe investigate the neighbourhood of interaction in a crowd: which neighbours influence a pedestrian's behaviour, how this depends on neighbour position, and howthe influences ofmultiple neighbours are combined. In three experiments, a participant walked in a virtual crowd whose speed and headingwere manipulated.We find that neighbour influence is linearly combined and decreases with distance, but not with lateral position (eccentricity).We model the neighbourhood as (i) a circularly symmetric region with (ii) aweighted average of neighbours, (iii) a uni-directional influence, and (iv)weights that decay exponentially to zero by 5 m. Themodel reproduces the experimental data and predicts individual trajectories in observational data on a human 'swarm'. The results yield the first bottom-up model of collective crowd motion. [ABSTRACT FROM AUTHOR]