Climate change is making our cities more vulnerable, increasing the needs for further policy actions to make them more resilient. In particular, the transport network is critical in the first phase of disaster response. This study presents the epirical findings of a large scale, nationwide analysis of the road network vulnerability in 69 Japanese cities. We (1) identify the level of network efficiency using topological elements in its undisturbed normal state; (2) evaluate the level of network robustness under different random and targeted attack scenarios; and (3) analyze the relationship of the identified network efficiency and robustness indicators with city-level characteristics. The main findings include: (1) cities with a higher population and a higher infrastructure investment tend to be more robust under random attacks; (2) larger cities tend to be less robust to targeted attacks, presumably due to their high agglomeration of urban functions; (3) car dependency tends to make cities more vulnerable toward random attacks and less vulnerable toward targeted attacks as it indicates a weaker concentration in urban functions; and (4) a high modal share for trains tends to make cities less vulnerable toward random events as it indicates a high agglomeration of urban functions. These findings will help policymakers to prioritize their budget allocations to improve nationwide disaster resilience.
Road networks are highly vulnerable to risks stemming from both internal factors, such as the topological structure of the network, and external factors, such as natural disasters. The disruptions caused by these potential risk factors could result in severe physical and socio-economic losses. Therefore, understanding the impact and risk associated with road networks will be beneficial in reducing losses and helping to prepare better risk mitigation and management strategies. This study proposes an integrated approach to assess risk of sediment hazard on the road network by borrowing concepts from (a) transport vulnerability studies, (b) disaster risk assessment, and (c) spatial risk analysis and applying it to an identified vulnerable road network in Kure, Japan. The proposed risk framework holistically incorporates the processes of topological network vulnerability analysis, exposure spatial analysis, hazard occurrence probability estimation through binary logit regression, impact calculation using Monte Carlo simulation, and risk estimation. Using the recorded information on the rainfall event and sediment disaster that occurred in Hiroshima prefecture in July 2018, 12,000 possible multi-link disruption scenarios were simulated. Spatial distribution of the risk calculations helped to identify links with high probability of disruption and high impact, that is, high-risk links. The findings of this study may support policy decisions on road risk mitigation and recovery prioritization during disaster and road infrastructure investment through risk-benefit analysis.
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