A microscopic evaluation of railway timetable robustness and critical points

Emma Solinen*, Gemma Nicholson, Anders Peterson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
254 Downloads (Pure)


One method to increase the quality of railway traffic flow is to construct a more robust timetable in which trains are able both to recover from delays and the delays are prevented from propagating. Previous research results show that the indicator Robustness in Critical Points (RCP) can be used to increase timetable robustness. In this paper we present the use of a method for RCP optimization: how it can be assessed ex-post via microscopic simulation. From the evaluation we learn more about how increased RCP values influence a timetable's performance. The aim is to understand more about RCP increase at a localised level within a timetable in terms of effects to the pairs of trains that are part of the indicator. We present a case study where an initial timetable and a timetable with increased RCP values are evaluated. The ex-post evaluation includes the quantification of measures concerning train-borne delay and robustness of operations, as well as measures capturing the subsequent quality of service experienced by passengers to assess the broader effects of improved robustness. The result shows that it is necessary to use several key performance indicators (KPIs) to evaluate the effects of an RCP increase. The robustness increases at a localised level, but the results also indicate that there is a need to analyse the relationship between ex-post measures and RCP further, to improve the method used to increase RCP and thus its overall effect on timetable robustness.

Original languageEnglish
JournalJournal of Rail Transport Planning and Management
Early online date1 Sept 2017
Publication statusE-pub ahead of print - 1 Sept 2017


  • Key performance indicators
  • Microscopic simulation
  • Railway timetabling
  • Robustness

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computer Science Applications


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