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      From Mindless Masses to Small Groups: Conceptualizing Collective Behavior in Crowd Modeling

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          Abstract

          Computer simulations are increasingly used to monitor and predict behavior at large crowd events, such as mass gatherings, festivals and evacuations. We critically examine the crowd modeling literature and call for future simulations of crowd behavior to be based more closely on findings from current social psychological research. A systematic review was conducted on the crowd modeling literature ( N = 140 articles) to identify the assumptions about crowd behavior that modelers use in their simulations. Articles were coded according to the way in which crowd structure was modeled. It was found that 2 broad types are used: mass approaches and small group approaches. However, neither the mass nor the small group approaches can accurately simulate the large collective behavior that has been found in extensive empirical research on crowd events. We argue that to model crowd behavior realistically, simulations must use methods which allow crowd members to identify with each other, as suggested by self-categorization theory.

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          Most cited references146

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          THE MORAL ECONOMY OF THE ENGLISH CROWD IN THE EIGHTEENTH CENTURY

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            The Walking Behaviour of Pedestrian Social Groups and Its Impact on Crowd Dynamics

            Human crowd motion is mainly driven by self-organized processes based on local interactions among pedestrians. While most studies of crowd behaviour consider only interactions among isolated individuals, it turns out that up to 70% of people in a crowd are actually moving in groups, such as friends, couples, or families walking together. These groups constitute medium-scale aggregated structures and their impact on crowd dynamics is still largely unknown. In this work, we analyze the motion of approximately 1500 pedestrian groups under natural condition, and show that social interactions among group members generate typical group walking patterns that influence crowd dynamics. At low density, group members tend to walk side by side, forming a line perpendicular to the walking direction. As the density increases, however, the linear walking formation is bent forward, turning it into a V-like pattern. These spatial patterns can be well described by a model based on social communication between group members. We show that the V-like walking pattern facilitates social interactions within the group, but reduces the flow because of its “non-aerodynamic” shape. Therefore, when crowd density increases, the group organization results from a trade-off between walking faster and facilitating social exchange. These insights demonstrate that crowd dynamics is not only determined by physical constraints induced by other pedestrians and the environment, but also significantly by communicative, social interactions among individuals.
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              How simple rules determine pedestrian behavior and crowd disasters.

              With the increasing size and frequency of mass events, the study of crowd disasters and the simulation of pedestrian flows have become important research areas. However, even successful modeling approaches such as those inspired by Newtonian force models are still not fully consistent with empirical observations and are sometimes hard to calibrate. Here, a cognitive science approach is proposed, which is based on behavioral heuristics. We suggest that, guided by visual information, namely the distance of obstructions in candidate lines of sight, pedestrians apply two simple cognitive procedures to adapt their walking speeds and directions. Although simpler than previous approaches, this model predicts individual trajectories and collective patterns of motion in good quantitative agreement with a large variety of empirical and experimental data. This model predicts the emergence of self-organization phenomena, such as the spontaneous formation of unidirectional lanes or stop-and-go waves. Moreover, the combination of pedestrian heuristics with body collisions generates crowd turbulence at extreme densities--a phenomenon that has been observed during recent crowd disasters. By proposing an integrated treatment of simultaneous interactions between multiple individuals, our approach overcomes limitations of current physics-inspired pair interaction models. Understanding crowd dynamics through cognitive heuristics is therefore not only crucial for a better preparation of safe mass events. It also clears the way for a more realistic modeling of collective social behaviors, in particular of human crowds and biological swarms. Furthermore, our behavioral heuristics may serve to improve the navigation of autonomous robots.
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                Author and article information

                Contributors
                Role: Editor-in-Chief
                Journal
                Rev Gen Psychol
                Rev Gen Psychol
                Review of General Psychology
                Educational Publishing Foundation
                1089-2680
                1939-1552
                17 August 2015
                September 2015
                : 19
                : 3
                : 215-229
                Affiliations
                [1 ]School of Psychology, University of Sussex
                [2 ]Department of Informatics, Centre for Computational Neuroscience and Robotics, University of Sussex
                Author notes
                We thank Professor Gerta Köster, Isabella von Sivers, Michael Seitz, Felix Dietrich, and Benedikt Zönnchen for their invaluable expertise and advice on crowd modeling. We also thank the Engineering and Physical Sciences Research Council (grant EP/L505109/1) for funding this research.
                [*] [* ]Correspondence concerning this article should be addressed to Anne Templeton, School of Psychology, Pevensey 1, 2D5, University of Sussex, Brighton, BN1 9RH, United Kingdom a.templeton@ 123456sussex.ac.uk
                Article
                gpr_19_3_215 2015-37512-001
                10.1037/gpr0000032
                4568938
                26388685
                51a008f7-2fb8-4fa9-967b-252dc1fe3758
                © 2015 The Author(s)

                This article has been published under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Copyright for this article is retained by the author(s). Author(s) grant(s) the American Psychological Association the exclusive right to publish the article and identify itself as the original publisher.

                History
                : 5 January 2015
                : 17 April 2015
                : 27 April 2015
                Categories
                Articles

                crowds,computer simulations,modeling,social identity,intragroup processes

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