Modeling crowd dynamics through coarse-grained data analysis
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 crowd traffic management systems, whereby observations of crowds can be cou...
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| Format: | Article |
| Language: | English |
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AIMS Press
2018-11-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2018059 |
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| author | Sebastien Motsch Mehdi Moussaïd Elsa G. Guillot Mathieu Moreau Julien Pettré Guy Theraulaz Cécile Appert-Rolland Pierre Degond |
| author_facet | Sebastien Motsch Mehdi Moussaïd Elsa G. Guillot Mathieu Moreau Julien Pettré Guy Theraulaz Cécile Appert-Rolland Pierre Degond |
| author_sort | Sebastien Motsch |
| collection | DOAJ |
| description | 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 crowd traffic management systems, whereby observations of crowds can be 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. |
| format | Article |
| id | doaj-art-57c112b8a01b4eab8d58a03a0cb11607 |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2018-11-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-57c112b8a01b4eab8d58a03a0cb116072025-01-24T02:41:08ZengAIMS PressMathematical Biosciences and Engineering1551-00182018-11-011561271129010.3934/mbe.2018059Modeling crowd dynamics through coarse-grained data analysisSebastien Motsch0Mehdi Moussaïd1Elsa G. Guillot2Mathieu Moreau3Julien Pettré4Guy Theraulaz5Cécile Appert-Rolland6Pierre Degond7School of Mathematical and Statistical Sciences, Arizona State University, Tempe, USAAdaptive Behavior and Cognition Group, Max Planck Institut for Human Development, Berlin, GermanyCentre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative (CBI), Centre National de la Recherche Scientifique (CNRS) & Université de Toulouse 3 Paul Sabatier, 31062 Toulouse, FranceCentre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative (CBI), Centre National de la Recherche Scientifique (CNRS) & Université de Toulouse 3 Paul Sabatier, 31062 Toulouse, FranceINRIA Rennes-Bretagne Atlantique, Campus de Beaulieu, Rennes, FranceCentre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative (CBI), Centre National de la Recherche Scientifique (CNRS) & Université de Toulouse 3 Paul Sabatier, 31062 Toulouse, FranceCNRS, Laboratoire de Physique Théorique, Orsay, FranceDepartment of Mathematics, Imperial College London, London SW7 2AZ, UKUnderstanding 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 crowd traffic management systems, whereby observations of crowds can be 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.https://www.aimspress.com/article/doi/10.3934/mbe.2018059pedestrian trafficbi-directional fluxcollective behaviourdata-based modelingmacroscopic model |
| spellingShingle | Sebastien Motsch Mehdi Moussaïd Elsa G. Guillot Mathieu Moreau Julien Pettré Guy Theraulaz Cécile Appert-Rolland Pierre Degond Modeling crowd dynamics through coarse-grained data analysis Mathematical Biosciences and Engineering pedestrian traffic bi-directional flux collective behaviour data-based modeling macroscopic model |
| title | Modeling crowd dynamics through coarse-grained data analysis |
| title_full | Modeling crowd dynamics through coarse-grained data analysis |
| title_fullStr | Modeling crowd dynamics through coarse-grained data analysis |
| title_full_unstemmed | Modeling crowd dynamics through coarse-grained data analysis |
| title_short | Modeling crowd dynamics through coarse-grained data analysis |
| title_sort | modeling crowd dynamics through coarse grained data analysis |
| topic | pedestrian traffic bi-directional flux collective behaviour data-based modeling macroscopic model |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2018059 |
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