Modeling 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

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)1271-1290
Number of pages20
JournalMathematical biosciences and engineering : MBE
Volume15
Issue number6
DOIs
StatePublished - Dec 1 2018

Fingerprint

data analysis
Data analysis
Modeling
Diptera
Walking
Accidents
traffic
Pedestrian Flow
Fundamental Diagram
Traffic Management
group behavior
Model
prediction
Collective Behavior
Optimization
Prediction
Urban Areas
Segregation
accidents
walking

Keywords

  • bi-directional flux
  • collective behaviour
  • data-based modeling
  • macroscopic model
  • Pedestrian traffic

ASJC Scopus subject areas

  • Modeling and Simulation
  • Agricultural and Biological Sciences(all)
  • Computational Mathematics
  • Applied Mathematics

Cite this

Motsch, S., Moussaïd, M., Guillot, E. G., Moreau, M., Pettré, J., Theraulaz, G., ... Degond, P. (2018). Modeling crowd dynamics through coarse-grained data analysis. Mathematical biosciences and engineering : MBE, 15(6), 1271-1290. https://doi.org/10.3934/mbe.2018059

Modeling crowd dynamics through coarse-grained data analysis. / Motsch, Sebastien; Moussaïd, Mehdi; Guillot, Elsa G.; Moreau, Mathieu; Pettré, Julien; Theraulaz, Guy; Appert-Rolland, Cécile; Degond, Pierre.

In: Mathematical biosciences and engineering : MBE, Vol. 15, No. 6, 01.12.2018, p. 1271-1290.

Research output: Contribution to journalArticle

Motsch, S, Moussaïd, M, Guillot, EG, Moreau, M, Pettré, J, Theraulaz, G, Appert-Rolland, C & Degond, P 2018, 'Modeling crowd dynamics through coarse-grained data analysis', Mathematical biosciences and engineering : MBE, vol. 15, no. 6, pp. 1271-1290. https://doi.org/10.3934/mbe.2018059
Motsch, Sebastien ; Moussaïd, Mehdi ; Guillot, Elsa G. ; Moreau, Mathieu ; Pettré, Julien ; Theraulaz, Guy ; Appert-Rolland, Cécile ; Degond, Pierre. / Modeling crowd dynamics through coarse-grained data analysis. In: Mathematical biosciences and engineering : MBE. 2018 ; Vol. 15, No. 6. pp. 1271-1290.
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