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Modeling crowd dynamics through coarse-grained data analysis


Sebastian Motsch

Mehdi Moussaid

Elsa Guillot

Matthieu Moreau


Guy Theraulaz


Pierre Degond


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.


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.

Journal Article Type Article
Acceptance Date Apr 25, 2018
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
Public URL
Publisher URL


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