  In animal societies as well as in human crowds, many observed collective
behaviours result from self-organized processes based on local interactions
among individuals. However, models of crowd dynamics are still lacking a
systematic individual-level experimental verification, and the local mechanisms
underlying the formation of collective patterns are not yet known in detail. We
have conducted a set of well-controlled experiments with pedestrians performing
simple avoidance tasks in order to determine the laws ruling their behaviour
during interactions. The analysis of the large trajectory dataset was used to
compute a behavioural map that describes the average change of the direction
and speed of a pedestrian for various interaction distances and angles. The
experimental results reveal features of the decision process when pedestrians
choose the side on which they evade, and show a side preference that is
amplified by mutual interactions. The predictions of a binary interaction model
based on the above findings were then compared to bidirectional flows of people
recorded in a crowded street. Simulations generate two asymmetric lanes with
opposite directions of motion, in quantitative agreement with our empirical
observations. The knowledge of pedestrian behavioural laws is an important step
ahead in the understanding of the underlying dynamics of crowd behaviour and
allows for reliable predictions of collective pedestrian movements under
natural conditions.
