  Pedestrian behaviours tend to depend on the type of facility. Therefore
accurate predictions of pedestrians movements in complex geometries (including
corridor, bottleneck or intersection) are difficult to achieve for classical
models with few parameters. Artificial neural networks have multiple parameters
and are able to identify various types of patterns. They could be a suitable
alternative for forecasts. We aim in this paper to present first steps testing
this approach. We compare estimations of pedestrian speed with a classical
model and a neural network for combinations of corridor and bottleneck
experiments. The results show that the neural network is able to differentiate
the two geometries and to improve the estimation of pedestrian speeds when the
geometries are mixed.
