A data-driven approach for quantifying the resilience of railway networks

Research output: Contribution to journalResearch articleContributedpeer-review


  • Max J. Knoester - , Delft University of Technology (Author)
  • Nikola Bešinović - , Chair of Railway Operations, Delft University of Technology (Author)
  • Amir Pooyan Afghari - , Delft University of Technology (Author)
  • Rob M.P. Goverde - , Delft University of Technology (Author)
  • Jochen van Egmond - , Traffic Management Division (Author)


Disruptions occur frequently in railway networks, requiring timetable adjustments, while causing serious delays and cancellations. However, little is known about the performance dynamics during disruptions nor the extent to which the resilience curve applies in practice. This paper presents a data-driven quantification approach for an ex-post assessment of the resilience of railway networks. Using historical traffic realization data in the Netherlands, resilience curves are reconstructed using a new composite indicator, and quantified for a large set of single disruptions. The values of the resilience metrics are compared across disruptions of different causes using Welch's ANOVA and the Games-Howell test. Additionally, representative resilience curves for each disruption cause are determined. Results show a significant heterogeneity in the shape of the resilience curves, even within disruptions of the same cause. The proposed approach represents a useful decision support tool for practitioners to assess disruptions dynamics and propose best measures to improve resilience.


Original languageEnglish
Article number103913
JournalTransportation Research Part A: Policy and Practice
Publication statusPublished - Jan 2024

External IDs

ORCID /0000-0003-4111-2255/work/149798007



  • ANOVA, Bathtub model, Data-driven, Disruption management, Railways, Resilience