Impacts of long-term service disruptions on passenger travel behaviour: A smart card analysis from the Greater Copenhagen area

Morten Eltved*, Nils Breyer, Jesper Bláfoss Ingvardson, Otto Anker Nielsen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Disruptions in public transport are a major source of frustration for passengers and result in lower public transport usage. Previous studies on the effect of disruptions on passenger travel behaviour have mainly focused on shorter disruptions, while the few studies on impacts of long-term disruptions have had limited focus on individual passenger behaviour. This paper fills the gap in research by proposing a novel methodology based on smart card data for analysing the impacts of long-term planned disruptions on passenger travel behaviour. We use k-means clustering to group passengers based on their travel behaviour before and after the closure. We can thus observe how different passenger groups changed travel behaviour after the disruption. We compare these observations to a group of reference lines without disruption to account for general trends. Using hierarchical clustering of daily travel patterns, we are able to in-depth analyse the reactions of certain passenger groups to the disruption. We apply the method on a 3-month closure of a rail line in the Greater Copenhagen area. The results suggest that, in particular, passengers with an everyday commuting behaviour have decreased after the disruption. The proposed methodology enables explicit analysis of the impact of disruptions on diverse passengers segments, while the specific results are useful for public transport agencies when planning long-term maintenance projects.
Original languageEnglish
Article number103198
JournalTransportation Research. Part C: Emerging Technologies
Volume131
ISSN0968-090X
DOIs
Publication statusPublished - 2021

Keywords

  • Public transport
  • Planned disruptions
  • Individual mobility
  • Smart card
  • Travel behaviour
  • Passenger segmentation

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