MSc Thesis Defence -Max Knoester
Title: A data-driven approach for evaluating the resilience of railway networks
MSc TIL thesis defense - Max Knoester
Date and time: Friday October 15, 2021 16:00-17:00 (public session)
Location: Lecture hall G, CEG
Teams link: https://tinyurl.com/master-defense-mknoester
Disruptions occur frequently in railway networks, requiring adjustments to the timetable, rolling stock planning and crew planning while causing delays and cancellations. Although the evolution of system performance during a disruption can be visualized in the resilience curve, not much is known about the exact shape of the curve or the extent to which it applies in practice. The limited quantitative knowledge about the resilience of railway networks makes it hard to design appropriate recovery measures. In this thesis, a data-driven evaluation approach is presented to make a post hoc assessment of the resilience of railway networks. Several resilience metrics are extracted from literature and two new resilience metrics are introduced. Resilience curves are plotted for a large and heterogeneous set of single disruptions and are quantified in terms of the resilience metrics. Among others, the values of the resilience metrics are compared across disruptions of different causes using Welch’s ANOVA and the Games-Howell test. The approach is applied on a case study of the Dutch railway network, with a focus on the five most common disruption causes. The results of the case study show that there is significant heterogeneity in the shape of the resilience curve, even within disruptions of the same cause. Train defects are found to be the least impactful disruptions on multiple resilience metrics, while collisions are found to be the most impactful disruptions. The successful application of the approach shows that it can be used to assess which types and which parts of disruptions deserve attention to improve disruption management practices.