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• W3033375810 startingPage "e0244206" @default.
• W3033375810 abstract "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." @default.
• W3033375810 created "2020-06-12" @default.
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• W3033375810 date "2020-12-21" @default.
• W3033375810 modified "2023-09-24" @default.
• W3033375810 title "StationRank: Aggregate dynamics of the Swiss railway" @default.
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• W3033375810 doi "https://doi.org/10.1371/journal.pone.0244206" @default.
• W3033375810 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7751885" @default.
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