Resilience or Robustness: Identifying Topological Vulnerabilities in Rail Networks


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.

Citation information

A Pagani, G Mosquera, A Alturki, S Johnson, S Jarvis, A Wilson, W Guo, L. Varga "Resilience or Robustness: Identifying Topological Vulnerabilities in Rail Networks,", 2018

Turing affiliated authors

Dr Weisi Guo

Honorary Professor at University of Warwick & Professor of Human Machine Intelligence at Cranfield University