Developing a context-based bounded centrality approach of street patterns in flooding: a case study of London

Xuhui Lin*, Qiuchen Lu, Neil Gunn, Simon Sølvsten

*Corresponding author for this work

    Research output: Contribution to conference without publisher/journalPaperResearchpeer-review

    Abstract

    Floods affect an average of 21 million people worldwide each year, and their frequency is expected to increase due to climate warming, population growth, and rapid urbanization. Previous research on the robustness of transportation networks during floods has mainly used percolation theory. However, the component size of disrupted networks cannot capture the entire network's information and, more importantly, does not reflect the local reality. To address this issue, this study introduces a novel approach to context-based bounded centrality to extract the local impact of disruption. In particular, we propose embedding travel behaviour into the road network to calculate bounded centrality and develop new measures characterizing connected component size during flooding. Our analysis can identify critical road segments during floods by comparing the decreasing trend and dispersibility of component size on road networks. To demonstrate the feasibility of these approaches, a case study of the London transportation infrastructure that integrates road networks with relevant urban contexts is presented in this paper. We find that this approach is beneficial for practical risk management, helping decision-makers allocate resources effectively in space and time.
    Original languageEnglish
    Publication date2023
    Publication statusPublished - 2023
    Event30th International Workshop on Intelligent Computing in Engineering - University College London, London, United Kingdom
    Duration: 4. Jul 20237. Jul 2023
    https://www.ucl.ac.uk/bartlett/construction/research/virtual-research-centres/institute-digital-innovation-built-environment/30th-eg-ice

    Conference

    Conference30th International Workshop on Intelligent Computing in Engineering
    LocationUniversity College London
    Country/TerritoryUnited Kingdom
    CityLondon
    Period04/07/202307/07/2023
    Internet address

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