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

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Názov: Developing a context-based bounded centrality approach of street patterns in flooding: a case study of London
Autori: Lin, Xuhui, Lu, Qiuchen, Gunn, Neil, Sølvsten, Simon
Informácie o vydavateľovi: 2023.
Rok vydania: 2023
Predmety: 13. Climate action, 11. Sustainability
Popis: 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.
Druh dokumentu: Conference object
Jazyk: English
Prístupová URL adresa: https://portal.findresearcher.sdu.dk/da/publications/709d1bb1-a24a-4427-9c09-6927cae50aad
Prístupové číslo: edsair.dedup.wf.002..a4a375bf5c7d734dc3a9c7be18dae5f6
Databáza: OpenAIRE
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  Data: Developing a context-based bounded centrality approach of street patterns in flooding: a case study of London
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  Data: <searchLink fieldCode="AR" term="%22Lin%2C+Xuhui%22">Lin, Xuhui</searchLink><br /><searchLink fieldCode="AR" term="%22Lu%2C+Qiuchen%22">Lu, Qiuchen</searchLink><br /><searchLink fieldCode="AR" term="%22Gunn%2C+Neil%22">Gunn, Neil</searchLink><br /><searchLink fieldCode="AR" term="%22Sølvsten%2C+Simon%22">Sølvsten, Simon</searchLink>
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  Data: 2023.
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  Data: 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.
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      – Text: English
    Subjects:
      – SubjectFull: 13. Climate action
        Type: general
      – SubjectFull: 11. Sustainability
        Type: general
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      – TitleFull: Developing a context-based bounded centrality approach of street patterns in flooding: a case study of London
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            NameFull: Lin, Xuhui
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            NameFull: Lu, Qiuchen
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            NameFull: Gunn, Neil
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            NameFull: Sølvsten, Simon
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              Y: 2023
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