  Increasing availability and quality of actual, as opposed to scheduled, open
transport data offers new possibilities for capturing the spatiotemporal
dynamics of the railway and other networks of social infrastructure. One way to
describe such complex phenomena is in terms of stochastic processes. At its
core, a stochastic model is domain-agnostic and algorithms discussed here have
been successfully used in other applications, including Google's PageRank
citation ranking. Our key assumption is that train routes constitute meaningful
sequences analogous to sentences of literary text. A corpus of routes is thus
susceptible to the same analytic tool-set as a corpus of sentences. With our
experiment in Switzerland, we introduce a method for building Markov Chains
from aggregated daily streams of railway traffic data. The stationary
distributions under normal and perturbed conditions are used to define systemic
risk measures with non-evident,valuable information about railway
infrastructure.
