  Network effects complicate demand forecasting in general, and outlier
detection in particular. For example, in transportation networks, sudden
increases in demand for a specific destination will not only affect the legs
arriving at that destination, but also connected legs nearby in the network.
Network effects are particularly relevant when transport service providers,
such as railway or coach companies, offer many multi-leg itineraries. In this
paper, we present a novel method for generating automated outlier alerts, to
support analysts in adjusting demand forecasts accordingly for reliable
planning. To create such alerts, we propose a two-step method for detecting
outlying demand from transportation network bookings. The first step clusters
network legs to appropriately partition and pool booking patterns. The second
step identifies outliers within each cluster to create a ranked alert list of
affected legs. We show that this method outperforms analyses that independently
consider each leg in a network, especially in highly-connected networks where
most passengers book multi-leg itineraries. We illustrate the applicability on
empirical data obtained from Deutsche Bahn and with a detailed simulation
study. The latter demonstrates the robustness of the approach and quantifies
the potential revenue benefits of adjusting for outlying demand in networks.
