  Rockfalls pose a substantial threat to ground transportation, due to their
rapidity, destructive potential and high probability of occurrence on steep
topographies, often found along roads and railway routes. Various factors
trigger rockfalls, including rainfall and seismic activity, and diverse
phenomena affect their probability of occurrence. Approaches for the assessment
of rockfall susceptibility range from purely phenomenological and statistical
methods, suitable for modeling large areas, to purely deterministic ones,
usually easier to use in local analyses. A common requirement is to locate
potential detachment points, often found uphill on cliffs, and the subsequent
assessment of the runout of rockfalls stemming from such points. Here, we apply
a physically based model for the calculation of rockfall trajectories along the
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 sources 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, within
the three-dimensional model STONE. The program provides a pixel-by-pixel
trajectory count, which we classify into a susceptibility map. We obtained the
map analyzing the railway track as a collection of segments, for which we
provide segment-wise rockfall susceptibility. Eventually, we considered an
equivalent graph representation of the network, which helps classifying
classifying the segments both on the basis of rockfall susceptibility and the
role of each segment in the network, resulting in a network-ranked
susceptibility.
