Modeling crowd dynamics through coarse-grained data analysis - Motsch,S; Moussaid,M; Guillot,E; Moreau,M; Pettré,J; Theraulaz,G; Appert-Rolland,C; Degond,PAA (Mathematical Biosciences and Engineering, 2018)
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.