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      Collective movements of pedestrians: How we can learn from simple experiments with non-human (ant) crowds

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          Abstract

          Introduction

          Understanding collective behavior of moving organisms and how interactions between individuals govern their collective motion has triggered a growing number of studies. Similarities have been observed between the scale-free behavioral aspects of various systems (i.e. groups of fish, ants, and mammals). Investigation of such connections between the collective motion of non-human organisms and that of humans however, has been relatively scarce. The problem demands for particular attention in the context of emergency escape motion for which innovative experimentation with panicking ants has been recently employed as a relatively inexpensive and non-invasive approach. However, little empirical evidence has been provided as to the relevance and reliability of this approach as a model of human behaviour.

          Methods

          This study explores pioneer experiments of emergency escape to tackle this question and to connect two forms of experimental observations that investigate the collective movement at macroscopic level. A large number of experiments with human and panicking ants are conducted representing the escape behavior of these systems in crowded spaces. The experiments share similar architectural structures in which two streams of crowd flow merge with one another. Measures such as discharge flow rates and the probability distribution of passage headways are extracted and compared between the two systems.

          Findings

          Our findings displayed an unexpected degree of similarity between the collective patterns emerged from both observation types, particularly based on aggregate measures. Experiments with ants and humans commonly indicated how significantly the efficiency of motion and the rate of discharge depend on the architectural design of the movement environment.

          Practical applications

          Our findings contribute to the accumulation of evidence needed to identify the boarders of applicability of experimentation with crowds of non-human entities as models of human collective motion as well as the level of measurements (i.e. macroscopic or microscopic) and the type of contexts at which reliable inferences can be drawn. This particularly has implications in the context of experimenting evacuation behaviour for which recruiting human subjects may face ethical restrictions. The findings, at minimum, offer promise as to the potential benefit of piloting such experiments with non-human crowds, thereby forming better-informed hypotheses.

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          Most cited references46

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          The principles of collective animal behaviour.

          In recent years, the concept of self-organization has been used to understand collective behaviour of animals. The central tenet of self-organization is that simple repeated interactions between individuals can produce complex adaptive patterns at the level of the group. Inspiration comes from patterns seen in physical systems, such as spiralling chemical waves, which arise without complexity at the level of the individual units of which the system is composed. The suggestion is that biological structures such as termite mounds, ant trail networks and even human crowds can be explained in terms of repeated interactions between the animals and their environment, without invoking individual complexity. Here, I review cases in which the self-organization approach has been successful in explaining collective behaviour of animal groups and societies. Ant pheromone trail networks, aggregation of cockroaches, the applause of opera audiences and the migration of fish schools have all been accurately described in terms of individuals following simple sets of rules. Unlike the simple units composing physical systems, however, animals are themselves complex entities, and other examples of collective behaviour, such as honey bee foraging with its myriad of dance signals and behavioural cues, cannot be fully understood in terms of simple individuals alone. I argue that the key to understanding collective behaviour lies in identifying the principles of the behavioural algorithms followed by individual animals and of how information flows between the animals. These principles, such as positive feedback, response thresholds and individual integrity, are repeatedly observed in very different animal societies. The future of collective behaviour research lies in classifying these principles, establishing the properties they produce at a group level and asking why they have evolved in so many different and distinct natural systems. Ultimately, this research could inform not only our understanding of animal societies, but also the principles by which we organize our own society.
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            Self-Organization and Collective Behavior in Vertebrates

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              Collective behavior in animal groups: theoretical models and empirical studies.

              Collective phenomena in animal groups have attracted much attention in the last years, becoming one of the hottest topics in ethology. There are various reasons for this. On the one hand, animal grouping provides a paradigmatic example of self-organization, where collective behavior emerges in absence of centralized control. The mechanism of group formation, where local rules for the individuals lead to a coherent global state, is very general and transcends the detailed nature of its components. In this respect, collective animal behavior is a subject of great interdisciplinary interest. On the other hand, there are several important issues related to the biological function of grouping and its evolutionary success. Research in this field boasts a number of theoretical models, but much less empirical results to compare with. For this reason, even if the general mechanisms through which self-organization is achieved are qualitatively well understood, a quantitative test of the models assumptions is still lacking. New analysis on large groups, which require sophisticated technological procedures, can provide the necessary empirical data.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                30 August 2017
                2017
                : 12
                : 8
                : e0182913
                Affiliations
                [001]Centre for Disaster Management and Public Safety, School of Engineering, The University of Melbourne, Australia
                Waseda University, JAPAN
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-4511-3191
                Article
                PONE-D-17-16056
                10.1371/journal.pone.0182913
                5576663
                28854221
                06ef6aa8-f406-4990-bbbe-a47bfd7e33da
                © 2017 Shahhoseini, Sarvi

                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.

                History
                : 26 April 2017
                : 26 July 2017
                Page count
                Figures: 8, Tables: 0, Pages: 20
                Funding
                Funded by: The study was financially supported by Arc Linkage project LP120200361, jointly provided by Public Transport Victoria (PTV) and Department of Economic Development, Jobs, Transport and Resources (DEDJTR)
                Award Recipient :
                The study was financially supported by Arc Linkage project LP120200361, jointly provided by Public Transport Victoria (PTV) and Department of Economic Development, Jobs, Transport and Resources (DEDJTR) Powered.
                Categories
                Research Article
                Biology and Life Sciences
                Organisms
                Animals
                Invertebrates
                Arthropoda
                Insects
                Hymenoptera
                Ants
                Biology and Life Sciences
                Psychology
                Collective Human Behavior
                Social Sciences
                Psychology
                Collective Human Behavior
                Biology and Life Sciences
                Behavior
                Biology and Life Sciences
                Behavior
                Animal Behavior
                Collective Animal Behavior
                Biology and Life Sciences
                Zoology
                Animal Behavior
                Collective Animal Behavior
                Earth Sciences
                Geography
                Human Geography
                Human Mobility
                Social Sciences
                Human Geography
                Human Mobility
                Physical Sciences
                Physics
                Classical Mechanics
                Continuum Mechanics
                Fluid Mechanics
                Fluid Dynamics
                Flow Rate
                Physical Sciences
                Mathematics
                Probability Theory
                Probability Distribution
                Biology and Life Sciences
                Organisms
                Animals
                Vertebrates
                Amniotes
                Mammals
                Rodents
                Mice
                Custom metadata
                All relevant data are included in the paper and its Supporting Information files.

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