StationRank: Aggregate dynamics of the Swiss railway

TL;DR

This paper introduces a novel method called StationRank that models Swiss railway traffic as Markov Chains to analyze the system's dynamics and assess infrastructure risk using real-time data.

Contribution

It presents a new approach to modeling railway networks with Markov Chains based on aggregated traffic data, enabling systemic risk analysis.

Findings

Markov Chain models effectively capture railway traffic dynamics.

StationRank reveals systemic risks under different operational conditions.

The approach leverages open transport data for infrastructure insights.

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.