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

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung


  • Max J. Knoester - , Technische Universität Delft (Autor:in)
  • Nikola Bešinović - , Professur für Betrieb von Bahnsystemen, Technische Universität Delft (Autor:in)
  • Amir Pooyan Afghari - , Technische Universität Delft (Autor:in)
  • Rob M.P. Goverde - , Technische Universität Delft (Autor:in)
  • Jochen van Egmond - , Traffic Management Division (Autor:in)


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.


FachzeitschriftTransportation Research Part A: Policy and Practice
PublikationsstatusVeröffentlicht - Jan. 2024

Externe IDs

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



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