Imperial College London

ProfessorPierreDegond

Faculty of Natural SciencesDepartment of Mathematics

Chair in Applied Mathematics
 
 
 
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Contact

 

+44 (0)20 7594 1474p.degond Website CV

 
 
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Location

 

6M38Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Motsch:2018:10.3934/mbe.2018059,
author = {Motsch, S and Moussaid, M and Guillot, E and Moreau, M and Pettré, J and Theraulaz, G and Appert-Rolland, C and Degond, PAA},
doi = {10.3934/mbe.2018059},
journal = {Mathematical Biosciences and Engineering},
pages = {1271--1290},
title = {Modeling crowd dynamics through coarse-grained data analysis},
url = {http://dx.doi.org/10.3934/mbe.2018059},
volume = {15},
year = {2018}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Understanding and predicting the collective behaviour of crowdsis essential to improve the efficiency of pedestrian flows in urban areas andminimize the risks of accidents at mass events. We advocate for the develop-ment of crowd traffic management systems, whereby observations of crowdscan be coupled to fast and reliable models to produce rapid predictions of thecrowd movement and eventually help crowd managers choose between tailoredoptimization strategies. Here, we propose a Bi-directional Macroscopic (BM)model as the core of such a system. Its key input is the fundamental diagramfor bi-directional flows, i.e. the relation between the pedestrian fluxes anddensities. We design and run a laboratory experiments involving a total of119 participants walking in opposite directions in a circular corridor and showthat the model is able to accurately capture the experimental data in a typicalcrowd forecasting situation. Finally, we propose a simple segregation strat-egy for enhancing the traffic efficiency, and use the BM model to determinethe conditions under which this strategy would be beneficial. The BM model,therefore, could serve as a building block to develop on the fly prediction ofcrowd movements and help deploying real-time crowd optimization strategies.
AU - Motsch,S
AU - Moussaid,M
AU - Guillot,E
AU - Moreau,M
AU - Pettré,J
AU - Theraulaz,G
AU - Appert-Rolland,C
AU - Degond,PAA
DO - 10.3934/mbe.2018059
EP - 1290
PY - 2018///
SN - 1547-1063
SP - 1271
TI - Modeling crowd dynamics through coarse-grained data analysis
T2 - Mathematical Biosciences and Engineering
UR - http://dx.doi.org/10.3934/mbe.2018059
UR - http://hdl.handle.net/10044/1/59298
VL - 15
ER -