Quantification and control of disruption propagation in multi-level public transport networks

Menno Yap, Oded Cats, Johanna Törnquist Krasemann, Niels van Oort, Serge Hoogendoorn

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
55 Downloads (Pure)

Abstract

Due to the multi-level nature of public transport networks, disruption impacts may spill-over beyond the primary effects occurring at the disrupted network level. During a public transport disruption, it is therefore important to quantify and control the disruption impacts for the total public transport network, instead of delimiting the analysis of their impacts to the public transport network level where this particular disruption occurs. We propose a modelling framework to quantify disruption impact propagation from the train network to the urban tram or bus network. This framework combines an optimisation-based train rescheduling model and a simulation-based dynamic public transport assignment model in an iterative procedure. The iterative process allows devising train schedules that take into account their impact on passenger flow re-distribution and related delays. Our study results in a framework which can improve public transport contingency plans on a strategic and tactical level in response to short- to medium-lasting public transport disruptions, by incorporating how the passenger impact of a train network disruption propagates to the urban network level. Furthermore, this framework allows for a more complete quantification of disruption costs, including their spilled-over impacts, retrospectively. We illustrate the successful implementation of our framework to a multi-level case study network in the Netherlands.
Original languageEnglish
Pages (from-to)83-106
Number of pages24
JournalInternational Journal of Transportation Science and Technology
Volume11
Issue number1
DOIs
Publication statusPublished - 2022

Keywords

  • Dynamic assignment
  • Optimisation
  • Public transport
  • Train rescheduling
  • Vulnerability analysis

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