Modeling crowd dynamics through coarse-grained data analysis

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Title: Modeling crowd dynamics through coarse-grained data analysis
Author(s): Motsch, S
Moussaid, M
Guillot, E
Moreau, M
Pettré, J
Theraulaz, G
Appert-Rolland, C
Degond, PAA
Item Type: Journal Article
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 develop- ment 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 strat- egy 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.
Publication Date: 1-Dec-2018
Date of Acceptance: 26-Apr-2018
ISSN: 1547-1063
Publisher: American Institute of Mathematical Sciences
Journal / Book Title: Mathematical Biosciences and Engineering
Copyright Statement: This paper is embargoed until publication. Once published it will be available fully open access.
Sponsor/Funder: The Royal Society
Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: WM130048
Copyright Statement: This paper is embargoed until publication. Once published it will be available fully open access.
Keywords: 0102 Applied Mathematics
0903 Biomedical Engineering
0904 Chemical Engineering
Publication Status: Accepted
Embargo Date: publication subject to indefinite embargo
Appears in Collections:Mathematics
Applied Mathematics and Mathematical Physics
Faculty of Natural Sciences

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