  Urban metro and tram networks are regularly subject to planned disruptions,
including closures, resulting from the need to maintain and renew
infrastructure. In this study, we first empirically analyse the passenger
demand response to planned public transport disruptions based on individual
passenger travel behaviour, based on which we infer generalised journey time
and cost elasticities for different passenger groups and time periods of the
day. Second, we develop a model which enables predicting public transport
demand for individual origin-destination pairs affected by a closure. The model
is trained based on the empirically observed travel behaviour. The proposed
method is applied to a case study closure in Amsterdam, the Netherlands, based
on which we empirically derive generalised journey time and generalised journey
cost elasticities. Our results suggest that passengers demand response is lower
for frequent users of the public transport network, as well as during weekdays,
especially during the peak periods. Arguably, this stems from a higher share of
captive passengers with a mandatory journey purpose in these segments, who will
continue making their journey nevertheless. During weekends, with typically
higher shares of leisure related journeys, a much more pronounced demand
response is found. The estimated neural network regression model is able to
predict passenger demand during public transport closures with a high level of
accuracy. This provides public transport agencies more precise insights into
the impact of closures on their revenue losses and on the potential need for
resources reallocation.
