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

•Method to analyse the impact of a planned disruption on different passenger groups using K-means clustering.•Daily individual travel behaviour analysis based on smart card data using hierarchical clustering.•Results show a loss of commuters on the disrupted line compared to passengers on reference line.•A large group of passengers that stop travelling during the disruption return afterwards. 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.