Empirical dynamics of railway delay propagation identified during the large-scale Rastatt disruption

Abstract

Transport networks are becoming increasingly large and interconnected. This interconnectivity is a key enabler of accessibility; on the other hand, it results in vulnerability, i.e. reduced performance, in case any specific part is subject to disruptions. We analyse how railway systems are vulnerable to delay, and how delays propagate in railway networks, studying real-life delay propagation phenomena on empirical data, determining real-life impact and delay propagation for the uncommon case of railway disruptions. We take a unique approach by looking at the same system, in two different operating conditions, to disentangle processes and dynamics that are normally present and co-occurring in railway operations. We exploit the unique chance to observe a systematic change in railway operations conditions, without a correspondent system change of infrastructure or timetable, coming from the occurrence of the large-scale disruption at Rastatt, Germany, in 2017. We define new statistical methods able to detect weak signals in the noisy dataset of recorded punctuality for passenger traffic in Switzerland, in the disrupted and undisrupted state, along a period of 1 year. We determine how delay propagation changed, and quantify the heterogeneous, large-scale cascading effects of the Rastatt disruption towards the Swiss network, hundreds of kilometers away. Operational measures of transport performance (i.e. punctuality and delays), while globally being very decreased, had a statistically relevant positive increase (though very geographically heterogeneous) on the Swiss passenger traffic during the disruption period. We identify two factors for this: (1) the reduced delay propagation at an international scale; and (2) to a minor extent, rerouted railway freight traffic; which show to combine linearly in the observed outcomes.

A high-speed railway network dataset from train operation records and weather data

“…The high-speed railway network dataset can be processed as the materials for effective methods to issue the problems in large-scale complex network, complex dynamical system, intelligent transportation, deep learning, data mining and other fields, including but not limited to complex network modeling 10 – 12 , complex dynamic system pattern mining 5 , 13 – 15 , travel demand analysis 16 , community detection and discovery 17 – 19 , urban accessibility research 20 , 21 , train delay analysis 6 , 7 , 22 – 24 , task mining on multi-scale and dynamic graphs 25 – 27 . In addition, it can be used to optimize the actual railway operation and management, such as (a) train operation scheme and schedule adjustment, (b) passenger service network improvement, (c) train speed, punctuality, capacity, and energy consumption prediction, (d) real-time dispatching, (e) intelligent driving assistance, (f) fault or accident detection and (g) maintenance plans making.…”

Modelling railway delay propagation as diffusion-like spreading

“…4 ICTEAM, Université catholique de Louvain, Louvain-la-Neuve, Belgium. 5 EMBL, Heidelberg, Germany. 6 Faculty of Physics, Warsaw University of Technology, Warsaw, Poland.…”

“…Small delays are often absorbed by built-in buffers and therefore do not have effects on larger scales [1,2]. However, from time to time, logistic disruptions -often caused by external factors like weather -lead to congestion or even a large-scale stand-still, with detrimental costs to society and economy [3][4][5][6].…”

Modelling railway delay propagation as diffusion-like spreading

“…4 jan.rombouts@kuleuven.be, Laboratory of Dynamics in Biological Systems, KU Leuven, Leuven, Belgium. 5 grzegorz.siudem@pw.edu.pl, Warsaw University of Technology, Faculty of Physics, Warsaw, Poland. 6 liubov.tupikina@cri-paris.org, Center for Research and Interdisciplinarity (CRI), Université de Paris, Paris, France.…”