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.