  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.
