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.

公共交通中断是乘客沮丧的主要来源，并导致公共交通使用率下降。先前关于中断对乘客旅行行为影响的研究主要集中在较短的中断上，而关于长期中断影响的少数研究很少关注个别乘客的行为。本文通过提出一种基于智能卡数据的新方法来分析长期计划中断对乘客旅行行为的影响，从而填补了研究空白。我们使用 k-means 聚类根据乘客关闭前后的旅行行为对他们进行分组。因此，我们可以观察到不同的乘客群体在中断后如何改变旅行行为。我们将这些观察结果与一组参考线进行比较，而不会中断以说明总体趋势。使用日常出行模式的层次聚类，我们能够深入分析某些乘客群体对中断的反应。我们将该方法应用于大哥本哈根地区的铁路线关闭 3 个月。结果表明，特别是在中断后，具有日常通勤行为的乘客减少了。拟议的方法可以明确分析中断对不同乘客群体的影响，而具体结果对于公共交通机构在规划长期维护项目时很有用。我们将该方法应用于大哥本哈根地区的铁路线关闭 3 个月。结果表明，特别是在中断后，具有日常通勤行为的乘客减少了。拟议的方法可以明确分析中断对不同乘客群体的影响，而具体结果对于公共交通机构在规划长期维护项目时很有用。我们将该方法应用于大哥本哈根地区的铁路线关闭 3 个月。结果表明，特别是在中断后，具有日常通勤行为的乘客减少了。拟议的方法可以明确分析中断对不同乘客群体的影响，而具体结果对于公共交通机构在规划长期维护项目时很有用。