  Many critical infrastructure systems have network structure and are under
stress. Despite their national importance, the complexity of large-scale
transport networks means we do not fully understand their vulnerabilities to
cascade failures. The research in this paper examines the interdependent rail
networks in Greater London and surrounding commuter area. We focus on the
morning commuter hours, where the system is under the most demand stress. There
is increasing evidence that the topological shape of the network plays an
important role in dynamic cascades. Here, we examine whether the different
topological measures of resilience (stability) or robustness (failure) are more
appropriate for understanding poor railway performance. The results show that
resilience and not robustness has a strong correlation to the consumer
experience statistics. Our results are a way of describing the complexity of
cascade dynamics on networks without the involvement of detailed
agent-based-models, showing that cascade effects are more responsible for poor
performance than failures. The network science analysis hints at pathways
towards making the network structure more resilient by reducing feedback loops.
