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 urbanisation. Previous
research on the robustness of transport networks during floods has mainly used percolation theory.
However, giant 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 bounded context-based 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 characterising the size of connected components
during flooding. Our analysis can identify critical road segments during floods by comparing the
decreasing trend and dispersibility of component sizes on road networks. To demonstrate the
feasibility of these approaches, a case study of London's transport infrastructure that integrates road
networks with relevant urban contexts was developed. This approach is beneficial for practical risk
management, helping decision-makers allocate resources efficiently in space and time
