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      Traffic Instabilities in Self-Organized Pedestrian Crowds


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          In human crowds as well as in many animal societies, local interactions among individuals often give rise to self-organized collective organizations that offer functional benefits to the group. For instance, flows of pedestrians moving in opposite directions spontaneously segregate into lanes of uniform walking directions. This phenomenon is often referred to as a smart collective pattern, as it increases the traffic efficiency with no need of external control. However, the functional benefits of this emergent organization have never been experimentally measured, and the underlying behavioral mechanisms are poorly understood. In this work, we have studied this phenomenon under controlled laboratory conditions. We found that the traffic segregation exhibits structural instabilities characterized by the alternation of organized and disorganized states, where the lifetime of well-organized clusters of pedestrians follow a stretched exponential relaxation process. Further analysis show that the inter-pedestrian variability of comfortable walking speeds is a key variable at the origin of the observed traffic perturbations. We show that the collective benefit of the emerging pattern is maximized when all pedestrians walk at the average speed of the group. In practice, however, local interactions between slow- and fast-walking pedestrians trigger global breakdowns of organization, which reduce the collective and the individual payoff provided by the traffic segregation. This work is a step ahead toward the understanding of traffic self-organization in crowds, which turns out to be modulated by complex behavioral mechanisms that do not always maximize the group's benefits. The quantitative understanding of crowd behaviors opens the way for designing bottom-up management strategies bound to promote the emergence of efficient collective behaviors in crowds.

          Author Summary

          A crowd of pedestrians is a complex system that exhibits a rich variety of self-organized collective behaviours. For instance, when two flows of people are walking in opposite directions in a crowded street, pedestrians spontaneously share the available space by forming lanes of uniform walking directions. This “pedestrian highway” is a typical example of self-organized functional pattern, as it increases the traffic efficiency with no need of external control. In this work, we have conducted a series of laboratory experiments to determine the behavioral mechanisms underlying this pattern. In contrast to previous theoretical predictions, we found that the traffic organization actually alternates in time between well-organized and disorganized states. Our results demonstrate that this unstable dynamics is due to interactions between people walking faster and slower than the average speed of the crowd. While the traffic efficiency is maximized when everybody walks at the same speed, crowd heterogeneity reduces the collective benefits provided by the traffic segregation. This work is a step ahead in understanding the mechanisms of crowd self-organization, and opens the way for the elaboration of management strategies bound to promote smart collective behaviors.

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          Most cited references 40

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          Novel Type of Phase Transition in a System of Self-Driven Particles

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            Collective cognition in animal groups.

             Iain Couzin (2009)
            The remarkable collective action of organisms such as swarming ants, schooling fish and flocking birds has long captivated the attention of artists, naturalists, philosophers and scientists. Despite a long history of scientific investigation, only now are we beginning to decipher the relationship between individuals and group-level properties. This interdisciplinary effort is beginning to reveal the underlying principles of collective decision-making in animal groups, demonstrating how social interactions, individual state, environmental modification and processes of informational amplification and decay can all play a part in tuning adaptive response. It is proposed that important commonalities exist with the understanding of neuronal processes and that much could be learned by considering collective animal behavior in the framework of cognitive science.
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              The logic of collective action: Public goods and the theory of groups


                Author and article information

                Role: Editor
                PLoS Comput Biol
                PLoS Comput. Biol
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                March 2012
                March 2012
                22 March 2012
                : 8
                : 3
                [1 ]Centre de Recherches sur la Cognition Animale, Université Paul Sabatier, Toulouse, France
                [2 ]CNRS, Centre de Recherches sur la Cognition Animale, Toulouse, France
                [3 ]Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, Berlin, Germany
                [4 ]Institut de Mathematiques de Toulouse, Université Paul Sabatier, Toulouse, France
                [5 ]CNRS, Institut de Mathématiques de Toulouse, Toulouse, France
                [6 ]INRIA Rennes - Bretagne Atlantique, Campus de Beaulieu, Rennes, France
                [7 ]Laboratoire de Physique Théorique, Université Paris Sud, Orsay, France
                [8 ]CNRS, Laboratoire de Physique Théorique, Orsay, France
                Tel Aviv University, Israel
                Author notes

                Conceived and designed the experiments: M. Moussaid, J. Pettré, C. Appert-Roland, P. Degond, G. Theraulaz. Performed the experiments: M. Moussaid, S. Lemercier, J. Pettré, C. Appert-Roland, G. Theraulaz. Analyzed the data: M. Moussaid, E.G. Guillot. Contributed reagents/materials/analysis tools: M. Moussaid, E.G. Guillot, M. Moreau, J. Fehrenbach, O. Chabiron. Wrote the paper: M. Moussaid, J. Fehrenbach, O. Chabiron, J. Pettré, C. Appert-Roland, P. Degond, G. Theraulaz.

                Moussaïd et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                Page count
                Pages: 10
                Research Article
                Computational Biology
                Social and Behavioral Sciences

                Quantitative & Systems biology


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