  Modal shift in public transport as a consequence of a disruption on a line
has in some cases unforeseen consequences such as an increase in congestion in
the rest of the network. How information is provided to users and their
behavior plays a central role in such configurations. We introduce here a
simple and stylised agent-based model aimed at understanding the impact of
behavioural parameters on modal shift. The model is applied on a case study
based on a stated preference survey for a segment of Paris suburban train
network. We systematically explore the parameter space and show non-trivial
patterns of congestion for some values of discrete choice parameters linked to
perceived wait time and congestion. We also apply a genetic optimisation
algorithm to the model to search for optimal compromises between congestion in
different modes.
