Modeling Pedestrians in an Airport Scenario with a Time-Augmented Petri Net.

In this paper time-augmented Petri nets are used to model people in the transit hall of an airport. Their behavior is strongly influenced by an event with a clear deadline (their flight), but typically there is so much time left that they linger and can be tempted to show random other behaviors, often induced by the location (encountering a coffee corner or a toilet). All behaviors are stochastic, but the firing rate is made a function of both location and time. This framework allows to show a rich set of behaviors; the diversity of the emergent behaviors is initiated with probabilities from observations in an actual transit hall of an airport.