predicting transitions across macroscopic states for railway systems.

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ID: 153237
2019
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Abstract
Railways are classic instances of complex socio-technical systems, whose defining characteristic is that they exist and function by integrating (continuous-time) interactions among technical components and human elements. Typically, unlike physical systems, there are no governing laws for describing their dynamics. Based purely on micro-unit data, here we present a data-driven framework to analyze macro-dynamics in such systems, leading us to the identification of specific states and prediction of transitions across them. It consists of three steps, which we elucidate using data from the Dutch railways. First, we form a dimensionally reduced phase-space by extracting a few relevant components, wherein relevance is proxied by dominance in terms of explained variance, as well as by persistence in time. Secondly, we apply a clustering algorithm to the reduced phase-space, resulting in the revelation of states of the system. Specifically, we identify 'rest' and 'disrupted' states, for which the system operations deviates respectively little and strongly from the planned timetable. Third, we define an early-warning metric based on the probability of transitions across states, predict whether the system is likely to transit from one state to another within a given time-frame and evaluate the performance of this metric using the Peirce skill score. Interestingly, using case studies, we demonstrate that the framework is able to predict large-scale disruptions up to 90 minutes beforehand with significant skill, demonstrating, for the railway companies, its potential to better track the evolution of large-scale disruptions in their networks. We discuss that the applicability of the three-step framework stretches to other systems as well-i.e., not only socio-technical ones-wherein real-time monitoring can help to prevent macro-scale state transitions, albeit the methods chosen to execute each step may depend on specific system-details.
Reference Key
dekker2019plospredicting Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Mark M Dekker;Debabrata Panja;Henk A Dijkstra;Stefan C Dekker
Journal ensaio pesquisa em educação em ciências
Year 2019
DOI
10.1371/journal.pone.0217710
URL
https://doi.org/10.1371/journal.pone.0217710
Keywords
medicinescience

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