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StationRank: Aggregate dynamics of the Swiss railway

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  • Georg Anagnostopoulos
  • Vahid Moosavi

Abstract

Increasing availability and quality of actual, as opposed to scheduled, open transport data offers new possibilities for capturing the spatiotemporal dynamics of 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.

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  • Georg Anagnostopoulos & Vahid Moosavi, 2020. "StationRank: Aggregate dynamics of the Swiss railway," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-17, December.
    • Handle: RePEc:plo:pone00:0244206
    DOI: 10.1371/journal.pone.0244206
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    References listed on IDEAS

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    1. Alexander Erath & Michael Löchl & Kay Axhausen, 2009. "Graph-Theoretical Analysis of the Swiss Road and Railway Networks Over Time," Networks and Spatial Economics, Springer, vol. 9(3), pages 379-400, September.
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