A topological characterization of flooding impacts on the Zurich road network
A topological characterization of flooding impacts on the Zurich road network
复制标题
洪水对苏黎世路网影响的拓扑特征
作者：
Y. Casali;H. Heinimann
基础设施系统是城市的结构支柱，促进基本服务的流动。由于这些系统可能会受到自然灾害的破坏，因此风险管理一直是评估后果和预期损害程度的普遍方法。尽管这可能是一个有价值的指标，但风险评估的实践并不代表危害如何在更大范围内影响资产网络。相反，网络拓扑指标非常有用，因为它们通过查看整个系统来评估网络基础设施的性能。正如此处所述，我们开始这项研究是为了加深对洪水事件如何影响道路网络拓扑特性的理解，在本例中是瑞士苏黎世的城市道路基础设施。使用洪水风险图，我们开发了一个程序来提取受损的网络并分析城市表面峰值水位的中心性指标。我们的方法将道路建模为边缘，将道路之间的交汇处建模为节点。介数中心性度量表征了网络内任何类型交换的节点或边的重要性，而紧密性中心性度量则衡量特定节点对所有其他节点的可访问性。这项调查产生了三个主要发现。首先，描述性分析表明节点和边的特征和模式在洪水事件下发生了变化。其次，随着洪水强度的增加，中心性度量的分布函数在尾部变得更重。第三，相关的应变将关键节点转移到这些节点在正常条件下并不重要的区域。这些发现对于确定关键地点和制定应对风险的计划至关重要。未来的项目可以扩展我们的方法，包括交通流量，使分析更接近真实世界的流量，并研究当地紧急情况下的可达性。
Infrastructure systems are the structural backbone of cities, facilitating the flow of essential services. Because those systems can be disrupted by natural hazards, risk management has been the prevailing approach for assessing the consequences and expected level of damage. Although this may be a valuable metric, the practice of risk assessment does not represent how hazards affect a network of assets on a larger scale. In contrast, network topology metrics are useful because they evaluate the performance of network infrastructures by looking at the system as a whole. As described here, we began this study to improve our understanding of how flooding events affect the topological properties of road networks, in this case, the urban road infrastructure of Zurich, Switzerland. Using maps of flooding risk, we developed a procedure to extract the damaged networks and analyze the centrality metrics for peak water levels on the surface of the city. Our approach modelled roads as edges and junctions between roads as nodes. The betweenness centrality metric characterizes the importance of nodes or edges for any type of exchange within a network, whereas the closeness centrality metric measures the accessibility of a specific node to all the other nodes. This investigation produced three main findings. First, descriptive analyses showed that the characteristics and patterns of nodes and edges changed under the flooding events. Second, the distribution function of centrality metrics became heavier in the tails as the flood magnitude increased. Third, the associated strain shifted critical nodes to areas in which those nodes would not be important under normal conditions. These findings are essential for identifying crucial locations and devising plans to address risks. Future projects could expand our approach by including traffic flow to move the analysis closer to real-world flows, and by studying the accessibility under emergency conditions at local levels.
DOI：
10.1016/j.physa.2006.01.051
发表时间：
2006-04-15
影响因子：
3.3
作者：
Lämmer, S;Gehlsen, B;Helbing, D
通讯作者：
Helbing, D