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.
| Original language | English |
|---|---|
| Article number | 181301 |
| Journal | Royal Society Open Science |
| Volume | 6 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 1 Feb 2019 |
Bibliographical note
Funding Information:Data accessibility. Data available from the Dryad Digital Repository: https://dx.doi.org/10.5061/dryad.6s76rp7 [42]. Authors’ contributions. W.G., L.V., S.Ja., S.Jo. and A.W. planned the experiments. A.P. and G.M. conducted the main numerical analysis. A.A. sourced the data and assisted in the analysis. A.P. and W.G. wrote the paper. All authors helped to analyse the findings and proof-read the paper. Competing interests. We declare we have no competing interests. Funding. The authors (W.G. and L.V.) acknowledge funding from EPSRC Engineering Complexity Resilience Network Plus (EP/N010019/1). The authors (W.G., S.Jo. and A.A.) acknowledge funding from EPSRC Centre for Doctoral Training in Urban Science and Progress (EP/L016400/1). The author (G.M.) acknowledges funding from EPSRC & MRC Centre for Doctoral Training in Mathematics for Real World Systems (EP/L015374/1). The authors (A.P.,
Funding Information:
The authors (W.G. and L.V.) acknowledge funding from EPSRC Engineering Complexity Resilience Network Plus (EP/N010019/1). The authors (W.G., S.Jo. and A.A.) acknowledge funding from EPSRC Centre for Doctoral Training in Urban Science and Progress (EP/L016400/1). The author (G.M.) acknowledges funding from EPSRC & MRC Centre for Doctoral Training in Mathematics for Real World Systems (EP/L015374/1). The authors (A.P., A.W. and W.G.) acknowledge funding from the Lloyd's Register Foundation's Programme for Data-Centric Engineering at The Alan Turing Institute.
Funding Information:
A.W. and W.G.) acknowledge funding from the Lloyd’s Register Foundation’s Programme for Data-Centric Engineering at The Alan Turing Institute. Acknowledgements. The authors acknowledge Office for National Statistics and the Office for Rail and Road for providing the data and TransportApi for journey planning.
Publisher Copyright:
© 2019 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License
Keywords
- Complex networks
- Resilience
- Rich-core club
- Robustness
- Trophic coherence
ASJC Scopus subject areas
- General