Railway transport system modelling approach for robustness analysis

The article presents an approach to train traffic modelling that allows for the analysis of how railway networks respond to various disturbances, including increased traffic and disturbance events. It discusses different methods of reconfiguration actions in key points of the railway network, which helps reduce delay propagation in the transport system. The 1st part covers building simulation models, which include defining infrastructure, setting train routes, configuring rolling stock, and disturbance scenarios, enabling the analysis of various disruptive events. The simulations allow for testing disturbance scenarios with minimal downtime risk without interfering with the real-world environment. The study results identified key system parameters generating the largest delays, such as platform availability, signaling, and the number of block sections. Probability density distributions for event intervals and durations were analyzed. The Kolmogorov–Smirnov test was used to confirm the fit of empirical distributions with theoretical ones, which were then implemented in the model of railway line No 271, running from Wrocław to Żmigród (Poland). As part of the reconfiguration of this railway line, new platforms were added, the time required for route setting was reduced, and the number of block sections was increased. These actions significantly reduced average delays, improved line capacity, and enhanced the robustness of the railway transport system against disturbances. The reconfiguration effectively reduced delays in areas causing significant time exceedances above 359 s, which was recognized in the Polish railway network as critical.

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Publication Details

Journal
Transport
Published
2024-12-31
DOI
https://doi.org/10.3846/transport.2024.22877
Citations
2
Primary Topic
Railway Systems and Energy Efficiency
Type
article
Field-Weighted Citation Impact
0.70
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article

Railway transport system modelling approach for robustness analysis

Łukasz Wolniewicz
2 citations
Transport
Railway Systems and Energy Efficiency
0.70
article

Railway transport system modelling approach for robustness analysis

Łukasz Wolniewicz
article en
2 citations

Abstract

The article presents an approach to train traffic modelling that allows for the analysis of how railway networks respond to various disturbances, including increased traffic and disturbance events. It discusses different methods of reconfiguration actions in key points of the railway network, which helps reduce delay propagation in the transport system. The 1st part covers building simulation models, which include defining infrastructure, setting train routes, configuring rolling stock, and disturbance scenarios, enabling the analysis of various disruptive events. The simulations allow for testing disturbance scenarios with minimal downtime risk without interfering with the real-world environment. The study results identified key system parameters generating the largest delays, such as platform availability, signaling, and the number of block sections. Probability density distributions for event intervals and durations were analyzed. The Kolmogorov–Smirnov test was used to confirm the fit of empirical distributions with theoretical ones, which were then implemented in the model of railway line No 271, running from Wrocław to Żmigród (Poland). As part of the reconfiguration of this railway line, new platforms were added, the time required for route setting was reduced, and the number of block sections was increased. These actions significantly reduced average delays, improved line capacity, and enhanced the robustness of the railway transport system against disturbances. The reconfiguration effectively reduced delays in areas causing significant time exceedances above 359 s, which was recognized in the Polish railway network as critical.

TransportVol. 39(4)
Wrocław University of Science and Technology (PL), AGH University of Krakow (PL)
Industry, innovation and infrastructure
Openalex Percentile: Top 24%
Railway Systems and Energy Efficiency
0.70
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