ArticleRoyal Society open science2019
Resilience or robustness: identifying topological vulnerabilities in rail networks.
Article in Royal Society open science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Optimal dismantling of directed networks.Nature communications · 2026Article
- Network accessibility as the emergence of cliques.Scientific reports · 2026Article
- Influence and influenceability: global directionality in directed complex networks.Royal Society open science · 2023Article
- Strong connectivity in real directed networks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Assessing the dynamic resilience of Urban Rail Transit Networks during their evolution using a ridership-weighted network.PloS one · 2023Article
- Geographic delay characterization of railway systems.Scientific reports · 2021Article
- Graph hierarchy: a novel framework to analyse hierarchical structures in complex networks.Scientific reports · 2021Article
- Article
- Organisational Social Influence on Directed Hierarchical Graphs, from Tyranny to Anarchy.Scientific reports · 2020Article
- Resilience or robustness: identifying topological vulnerabilities in rail networks.Royal Society open science · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many critical infrastructure systems have network structures and are under stress. Despite their national importance, the complexity of large-scale transport networks means that we do not fully understand their vulnerabilities to cascade failures. The research conducted through 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, not robustness, has a strong correlation with 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.