A Causal Inference Approach to Measure the Vulnerability of Urban Metro Systems
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Transit operators and passengers 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, thus obviates the need for assumptions made by previous studies on human behaviour and disruption scenarios. We develop three empirical vulnerability metrics based on the causal impact of disruptions on travel demand and the average travel speed. 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 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 reveals that passengers from outer London stations suffer from longer individual delays due to lack of alternative routes.
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