StationRank: Aggregate dynamics of the Swiss railway

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.

Iterative Train Scheduling under Disruption with Maximum Satisfiability

“…In the past, the impact of large disruptions on the overall public transportation network has been studied (Anagnostopoulos & Moosavi, 2020;Marra & Corman, 2020;Burgholzer et al, 2013). Marra and Corman (2020) used machine learning to identify patterns that impact the number and size of disruptions on the passenger's path.…”

“…The machine learning method analyzed the real public transportation networks of Zurich. Anagnostopoulos and Moosavi (2020) studied the systemic influence and fragility of all Swiss train stations to disruptions. The goal was to restructure routes and stations in order to reduce the fragility of the schedule.…”

“…The goal was to restructure routes and stations in order to reduce the fragility of the schedule. Anagnostopoulos and Moosavi (2020) showed that the most influential stations (the ones used by more train routes) are less fragile than remote stations. In this work, we focus on finding the most common disruptions, their characteristics, and their causes, aiming to create a realistic benchmark to test our re-solving algorithm.…”