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Urban metro and tram networks are regularly subject to planned disruptions,including closures, resulting from the need to maintain and renewinfrastructure. In this study, we first empirically analyse the passengerdemand response to planned public transport disruptions based on individualpassenger travel behaviour, based on which we infer generalised journey timeand cost elasticities for different passenger groups and time periods of theday. Second, we develop a model which enables predicting public transportdemand for individual origin-destination pairs affected by a closure. The modelis trained based on the empirically observed travel behaviour. The proposedmethod is applied to a case study closure in Amsterdam, the Netherlands, basedon which we empirically derive generalised journey time and generalised journeycost elasticities. Our results suggest that passengers demand response is lowerfor frequent users of the public transport network, as well as during weekdays,especially during the peak periods. Arguably, this stems from a higher share ofcaptive passengers with a mandatory journey purpose in these segments, who willcontinue making their journey nevertheless. During weekends, with typicallyhigher shares of leisure related journeys, a much more pronounced demandresponse is found. The estimated neural network regression model is able topredict passenger demand during public transport closures with a high level ofaccuracy. This provides public transport agencies more precise insights intothe impact of closures on their revenue losses and on the potential need forresources reallocation.

Analysis and Prediction of Ridership Impacts during Planned Public Transport Disruptions