  Rockfalls pose a substantial threat to ground transportation, due to their
rapidity, destructive potential and high probability of occurrence on steep
topographies, found along roads and railways. Approaches for assessment of
rockfall susceptibility range from purely phenomenological methods and
statistical methods, suitable for modeling large areas, to purely deterministic
ones, usually easier to use in local analyses. A common requirement is the need
to locate potential detachment points, often found uphill on cliffs, and the
subsequent assessment of the runout areas of rockfalls stemming from such
points. Here, we apply a physically based model to calculate rockfall
trajectories along the whole Italian railway network, within a corridor of
total length of about 17,000 km and varying width. We propose a data-driven
method for the location of rockfall source points based on expert mapping of
potential source areas on sample representative locations. Using empirical
distributions of gridded slope values in source areas mapped by experts, we
derived probabilistic maps of rockfall sources in the proximity of the railway
network, regardless of a particular trigger. Source areas act as starting
points of simulated trajectories in the three-dimensional model STONE. The
program provides a pixel-by-pixel trajectory count, covering 24,500 km2, the
largest homogeneous application of the model to date. We classified the map
into a vector susceptibility map of the segments of the railway, for which we
provide segment-wise rockfall susceptibility. Eventually, we considered a graph
representation of the network to classify the segments both on the basis of
rockfall susceptibility and the role of each segment in the network, resulting
in a network-ranked susceptibility. Both maps are useful for subsequent hazard
assessment, and to prioritize safety improvements along the railway, at
national scale.
