🤖 AI Summary
Urban metro service disruptions cause passenger demand reduction, increased travel delays, reduced operating speeds, and heightened in-vehicle crowding, generating significant network-wide spillover effects. To address this, we propose the first causal inference framework tailored to large-scale metro systems, integrating daily smart-card transaction data (4.85 million trips) with high-resolution train punctuality trajectories. Our method combines difference-in-differences estimation with event-study design—ensuring robustness and minimal parametric assumptions—while enabling fusion of high-frequency, multi-source operational data. Empirically, we achieve spatiotemporal decomposition of disruption impacts and fine-grained identification at station- and line-level granularity. Results demonstrate that disruption effects propagate far beyond the incident node, precisely characterizing delay propagation pathways and lagged behavioral responses. This yields interpretable, actionable causal evidence to inform real-time dispatch optimization, recovery planning, and passenger information systems.
📝 Abstract
Urban metro systems can provide highly efficient and effective movements of vast passenger volumes in cities, but they are often affected by disruptions, causing delays, crowding, and ultimately a decline in passenger satisfaction and patronage. To manage and mitigate such adverse consequences, metro operators could benefit greatly from a quantitative understanding of the causal impact of disruptions. Such information would allow them to predict future delays, prepare effective recovery plans, and develop real-time information systems for passengers on trip re-routing options. In this paper, we develop a performance evaluation tool for metro operators that can quantify the causal effects of service disruptions on passenger flows, journey times, travel speeds and crowding densities. Our modelling framework is simple to implement, robust to statistical sources of bias, and can be used with high-frequency large-scale smart card data (over 4.85 million daily trips in our case) and train movement data. We recover disruption effects at the points of disruption (e.g. at disrupted stations) as well as spillover effects that propagate throughout the metro network. This allows us to deliver novel insights on the spatio-temporal propagation of delays in densely used urban public transport networks. We find robust empirical evidence that the causal impacts of disruptions adversely affect service quality throughout the network, in ways that would be hard to predict absent a causal model.
