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Modeling crowd dynamics through coarse-grained data analysis

Motsch, Sebastian; Moussaid, Mehdi; Guillot, Elsa; Moreau, Matthieu; Pettré, Julien; Theraulaz, Guy; Appert-Rolland, Cécile; Degond, Pierre

Authors

Sebastian Motsch

Mehdi Moussaid

Matthieu Moreau

Julien Pettré

Guy Theraulaz

Cécile Appert-Rolland

Pierre Degond



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 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.

Citation

Motsch, S., Moussaid, M., Guillot, E., Moreau, M., Pettré, J., Theraulaz, G., …Degond, P. (in press). Modeling crowd dynamics through coarse-grained data analysis. Mathematical Biosciences and Engineering, 15(6), 1271-1290. https://doi.org/10.3934/mbe.2018059

Journal Article Type Article
Acceptance Date Apr 25, 2018
Publicly Available Date Nov 30, -0001
Journal Mathematical Biosciences and Engineering
Print ISSN 1547-1063
Publisher American Institute of Mathematical Sciences
Peer Reviewed Peer Reviewed
Volume 15
Issue 6
Pages 1271-1290
DOI https://doi.org/10.3934/mbe.2018059.
Public URL https://uwe-repository.worktribe.com/output/869063
Publisher URL http://www.aimspress.com/MBE/2018/6/1271

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