Causal resilience curves: A data-driven framework for quantifying the spatiotemporal impacts of metro service disruptions

TL;DR

This paper introduces a data-driven causal inference framework using smart card data to quantify the spatiotemporal impacts of metro service disruptions, providing actionable resilience insights.

Contribution

It presents the first causal estimates of dynamic metro resilience using a synthetic control approach with high-frequency data, improving accuracy over traditional methods.

Findings

Accurately measures direct and spillover effects of disruptions

Reveals spatial heterogeneity in resilience patterns

Outperforms naive and machine learning benchmarks

Abstract

Urban metro systems move vast numbers of passengers with a high level of efficiency in resource use, but frequently experience disruptions that result in delays, crowding, and deterioration in passenger satisfaction and patronage. To quantify these adverse consequences, this paper presents a novel, data-driven causal inference framework to measure metro resilience by estimating both the direct and spillover effects of service disruptions on passenger demand, journey time, travel speed and on-board crowding. By integrating high-frequency smart card data into a synthetic control design, we use weighted non-disrupted days to construct unbiased counterfactuals, which resolves confounding factors and accurately captures disruption propagation across the network. The impact estimates are further translated into station-level causal resilience curves that reveal spatial heterogeneity in the temporal patterns of degradation and recovery across locations, providing metro operators with actionable insights for targeted interventions and resource allocation. A case study of the Hong Kong MTR demonstrates the framework's superiority over naive typical-day comparisons and machine-learning benchmarks in delivering unbiased resilience curves. This paper is the first to derive causal estimates of dynamic metro resilience. This practical tool can be generalised to evaluate resilience in a broad range of public transport systems.