A causal inference approach to measure the vulnerability of urban metro systems

Bibliographic Data

ID19338514
AuthorsNan Zhang (0000-0002-8032-275X, Imperial College London), Daniel J Graham (0000-0002-6971-8737, Imperial College London, corresponding author), Daniel Hörcher (0000-0003-3070-8096, Imperial College London), Prateek Bansal (0000-0001-6851-8461, Imperial College London)
Year2021
Volume48
Issue6
Pages3269-3300
Publication date2021-12-15
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueTransportation (JOURNAL)
Journal identifiersISSN: 0049-4488 • E-ISSN: 1572-9435
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s11116-020-10152-6
OpenAlexW3124263360
LanguageEN
Citations received6
References cited40

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

Causal inference · Computer security · Econometrics · Economics · Matching (statistics) · Operations research · Population · Statistics · Transport engineering · Vulnerability (computing) · Vulnerability assessment · Computer Science · Engineering · Infrastructure Resilience and Vulnerability Analysis · Mathematics · Transportation Planning and Optimization · Urban Transport and Accessibility

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