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In animal societies as well as in human crowds, many observed collectivebehaviours result from self-organized processes based on local interactionsamong individuals. However, models of crowd dynamics are still lacking asystematic individual-level experimental verification, and the local mechanismsunderlying the formation of collective patterns are not yet known in detail. Wehave conducted a set of well-controlled experiments with pedestrians performingsimple avoidance tasks in order to determine the laws ruling their behaviourduring interactions. The analysis of the large trajectory dataset was used tocompute a behavioural map that describes the average change of the directionand speed of a pedestrian for various interaction distances and angles. Theexperimental results reveal features of the decision process when pedestrianschoose the side on which they evade, and show a side preference that isamplified by mutual interactions. The predictions of a binary interaction modelbased on the above findings were then compared to bidirectional flows of peoplerecorded in a crowded street. Simulations generate two asymmetric lanes withopposite directions of motion, in quantitative agreement with our empiricalobservations. The knowledge of pedestrian behavioural laws is an important stepahead in the understanding of the underlying dynamics of crowd behaviour andallows for reliable predictions of collective pedestrian movements undernatural conditions.

Experimental study of the behavioural mechanisms underlying self-organization in human crowds