Transit operators need vulnerability measures to understand the level of service degradation under disruptions. This paper contributes to the literature with a novel causal inference approach for estimating station-level vulnerability in metro systems. The empirical analysis is based on large-scale data on historical incidents and population-level passenger demand. This analysis thus obviates the need for assumptions made by previous studies on human behaviour and disruption scenarios. We develop four empirical vulnerability metrics based on the causal impact of disruptions on travel demand, average travel speed and passenger flow distribution. Specifically, the proposed metrics based on the irregularity in passenger flow distribution extends the scope of vulnerability measurement to the entire trip distribution, instead of just analysing the disruption impact on the entry or exit demand (that is, moments of the trip distribution). The unbiased estimates of disruption impact are obtained by adopting a propensity score matching method, which adjusts for the confounding biases caused by non-random occurrence of disruptions. An application of the proposed framework to the London Underground indicates that the vulnerability of a metro station depends on the location, topology, and other characteristics. We find that, in 2013, central London stations are more vulnerable in terms of travel demand loss. However, the loss of average travel speed and irregularity in relative passenger flows reveal that passengers from outer London stations suffer from longer individual delays due to lack of alternative routes.

运输运营商需要采取脆弱性措施，以了解中断造成的服务降级水平。本文通过一种新颖的因果推论方法为文献做出了贡献，以估算地铁系统中的站级脆弱性。实证分析是基于有关历史事件和人口水平的旅客需求的大规模数据。因此，该分析消除了对先前关于人类行为和破坏情景的研究所作假设的需要。我们基于干扰对旅行需求，平均旅行速度和客流分布的因果影响，开发了四个经验脆弱性指标。具体而言，基于客流分配不规则性提出的指标将漏洞度量的范围扩展到整个行程分布，而不仅仅是分析对进入或退出需求（即行程分布的时刻）的中断影响。干扰影响的无偏估计是通过采用倾向得分匹配方法获得的，该方法可对由非随机发生的干扰引起的混杂偏差进行调整。提议的框架在伦敦地铁上的应用表明地铁站的脆弱性取决于位置，拓扑和其他特征。我们发现，2013年，伦敦市中心车站在旅行需求损失方面更加脆弱。然而，