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Forecasting crowd dynamics through coarse-grained data analysis

Sebastien Motsch, Mehdi Moussaïd, Elsa G. Guillot, Mathieu Moreau, Julien Pettré, Guy Theraulaz, Cécile Appert-Rolland, Pierre Degond
doi: https://doi.org/10.1101/175760
Sebastien Motsch
1School of Mathematical and Statistical Sciences, Arizona State University, Tempe, USA,
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  • For correspondence: smotsch@asu.edu
Mehdi Moussaïd
2Adaptive Behavior and Cognition Group, Max Planck Institut for Human Development, Berlin, Germany,
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  • For correspondence: moussaid@mpib-berlin.mpg.de
Elsa G. Guillot
3CNRS, Centre de Recherches sur la Cognition Animale, UMR-CNRS 5169, Toulouse, France, Université Paul Sabatier, Toulouse, France, , ,
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  • For correspondence: elsa.guillot@unil.ch mathieu.moreau1@univ-tlse3.fr guy.theraulaz@univ-tlse3.fr
Mathieu Moreau
3CNRS, Centre de Recherches sur la Cognition Animale, UMR-CNRS 5169, Toulouse, France, Université Paul Sabatier, Toulouse, France, , ,
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  • For correspondence: elsa.guillot@unil.ch mathieu.moreau1@univ-tlse3.fr guy.theraulaz@univ-tlse3.fr
Julien Pettré
4INRIA Rennes-Bretagne Atlantique, Campus de Beaulieu, Rennes, France,
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  • For correspondence: julien.pettre@inria.fr
Guy Theraulaz
3CNRS, Centre de Recherches sur la Cognition Animale, UMR-CNRS 5169, Toulouse, France, Université Paul Sabatier, Toulouse, France, , ,
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  • For correspondence: elsa.guillot@unil.ch mathieu.moreau1@univ-tlse3.fr guy.theraulaz@univ-tlse3.fr
Cécile Appert-Rolland
5CNRS, Laboratoire de Physique Théorique, Orsay, France,
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  • For correspondence: Cecile.Appert-Rolland@th.u-psud.fr
Pierre Degond
6Department of Mathematics, Imperial College London, London SW7 2AZ, UK,
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  • For correspondence: p.degond@imperial.ac.uk
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Abstract

Understanding and predicting the collective behaviour of crowds is essential to improve the efficiency of pedestrian flows in urban areas and minimize the risks of accidents at mass events. We advocate for the development of a & “crowd forecasting system„whereby real-time observations of crowds are coupled to fast and reliable models to produce rapid predictions of the crowd movement and eventually help crowd managers choose between tailored optimization strategies. Here, we propose a Bi-directional Macroscopic (BM) model as the core of such a system. Its key input is the fundamental diagram for bi-directional flows, i.e. the relation between the pedestrian fluxes and densities. We design and run a laboratory experiments involving a total of 119 participants walking in opposite directions in a circular corridor and show that the model is able to accurately capture the experimental data in a typical crowd forecasting situation. Finally, we propose a simple segregation strategy for enhancing the traffic efficiency, and use the BM model to determine the conditions under which this strategy would be beneficial. The BM model, therefore, could serve as a building block to develop on the fly prediction of crowd movements and help deploying real-time crowd optimization strategies.

Footnotes

  • AMS Subject classification: Primary: 93A30, 90B20; Secondary: 35B30, 35L65.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted August 30, 2017.
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Forecasting crowd dynamics through coarse-grained data analysis
Sebastien Motsch, Mehdi Moussaïd, Elsa G. Guillot, Mathieu Moreau, Julien Pettré, Guy Theraulaz, Cécile Appert-Rolland, Pierre Degond
bioRxiv 175760; doi: https://doi.org/10.1101/175760
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Forecasting crowd dynamics through coarse-grained data analysis
Sebastien Motsch, Mehdi Moussaïd, Elsa G. Guillot, Mathieu Moreau, Julien Pettré, Guy Theraulaz, Cécile Appert-Rolland, Pierre Degond
bioRxiv 175760; doi: https://doi.org/10.1101/175760

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