Modeling crowd dynamics through coarse-grained data analysis

Understanding and predicting the collective behaviour of crowds is essential to improve the effciency of pedestrian ows in urban areas and minimize the risks of accidents at mass events. We advocate for the development of crowd traffc 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 ows, i.e. the relation between the pedestrian uxes 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 traffc effciency, and use the BM model to determine the conditions under which this strategy would be bene cial. The BM model, therefore, could serve as a building block to develop on the y prediction of crowd movements and help deploying real-time crowd optimization strategies.