English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Transit operators need vulnerability measures to understand the level ofservice degradation under disruptions. This paper contributes to the literaturewith a novel causal inference approach for estimating station-levelvulnerability in metro systems. The empirical analysis is based on large-scaledata on historical incidents and population-level passenger demand. Thisanalysis thus obviates the need for assumptions made by previous studies onhuman behaviour and disruption scenarios. We develop four empiricalvulnerability metrics based on the causal impact of disruptions on traveldemand, average travel speed and passenger flow distribution. Specifically, theproposed metrics based on the irregularity in passenger flow distributionextends 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 ofdisruption impact are obtained by adopting a propensity score matching method,which adjusts for the confounding biases caused by non-random occurrence ofdisruptions. An application of the proposed framework to the London Undergroundindicates that the vulnerability of a metro station depends on the location,topology, and other characteristics. We find that, in 2013, central Londonstations are more vulnerable in terms of travel demand loss. However, the lossof average travel speed and irregularity in relative passenger flows revealthat passengers from outer London stations suffer from longer individual delaysdue to lack of alternative routes.

A Causal Inference Approach to Measure the Vulnerability of Urban Metro Systems