Markov chain model for delay distribution in train schedules: Assessing the effectiveness of time allowances

Şahin, İsmail

doi:10.1016/j.jrtpm.2017.08.006

Cited by 50 publications

(28 citation statements)

References 6 publications

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“…Exponential scaling models or epidemic spreading models could be further extended to include the observed performance-depending vulnerability effects. Simulation 51 , 52 or optimization models 53 need to bridge the large gap between detail and complexity of microscopic and macroscopic studies, which we only scratched in this study 54 .…”

Section: Discussionmentioning

confidence: 99%

“…Buffer times in timetables are specific to public transport and railway systems, not observed in topological studies; private modes; and also not in airline or maritime networks in the same extent, as trains have multiple stops closely spaced. By suitable choice of buffer times in a timetable, delays can disappear or magnify over time, and specific stations might have smaller (respectively larger) delays without any specific event as cause 52 . The effect of buffer times can be approximated as a systematic baseline of delay; plus a non-linear noise, which affects and reduces delay differently for punctual and non-punctual traffic.…”

Section: Methodsmentioning

confidence: 99%

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

Büchel

Spanninger

Corman

2020

Sci Rep

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Büchel

Spanninger

Corman

2020

Sci Rep

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“…We study the dynamics of the Swiss railway network over a period of one month via characteristics of Markov Chains (MC) [14]. Originally conceived for statistical analysis of texts, MC have proven useful in various fields such as search engines [4], traffic dynamics [9][10][11]15] and econometrics [12,13]. More formally, MC are defined as stochastic processes that display the Markov Property…”

Section: The Methodsmentioning

confidence: 99%

StationRank: Aggregate dynamics of the Swiss railway

Anagnostopoulos

Moosavi

2020

PLoS ONE

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Increasing availability and quality of actual, as opposed to scheduled, open transport data offers new possibilities for capturing the spatiotemporal dynamics of railway and other networks of social infrastructure. One way to describe such complex phenomena is in terms of stochastic processes. At its core, a stochastic model is domain-agnostic and algorithms discussed here have been successfully used in other applications, including Google’s PageRank citation ranking. Our key assumption is that train routes constitute meaningful sequences analogous to sentences of literary text. A corpus of routes is thus susceptible to the same analytic tool-set as a corpus of sentences. With our experiment in Switzerland, we introduce a method for building Markov Chains from aggregated daily streams of railway traffic data. The stationary distributions under normal and perturbed conditions are used to define systemic risk measures with non-evident, valuable information about railway infrastructure.

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“…The application of the URRAN methodology, which is considered in CVPX-2016, is an attempt to consider the risk level of a failure probability (λ) of technical means during their operation [7,8].…”

Section: Methodsmentioning

confidence: 99%

The determination of the repairs of railway track considering the reliability and risk level

Grin

Kovalenko

2020

E3S Web Conf.

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The method of predictive calculations of the state of the rails as the main element of assessing the state of the track when planning major repairs of the track is given. With a standard service life of 600 to 700 million tons gross, it is necessary to predict the number of defective rails on the track when increasing the service life to 1100 million tons gross (considering local rail replacement during intermediate repairs). The polygon for assigning repairs is determined by the number of “Windows” that can be provided per week on the section. The projected increase in the work of the track causes the need to increase life cycle cost depreciation charges for the formation of the repair Fund for capital repairs by 30-35%.

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Markov chain model for delay distribution in train schedules: Assessing the effectiveness of time allowances

Cited by 50 publications

References 6 publications

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

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

StationRank: Aggregate dynamics of the Swiss railway

The determination of the repairs of railway track considering the reliability and risk level

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