Connectivity evaluation of large road network by capacity-weighted eigenvector centrality analysis
Connectivity evaluation of large road network by capacity-weighted eigenvector centrality analysis
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DOI：
10.1080/23249935.2020.1804480
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发表时间：
2020-09-03
影响因子：
3.3
通讯作者：
Cheung, Kam-Fung
中科院分区：
文献类型：
--
作者：
Ando, Hiroe;Bell, Michael;Cheung, Kam-Fung
评价网络稳健性的方法已被广泛研究。这种方法通常需要获得交通平衡条件或解决数学问题，而且这些方法只能应用于有限规模的网络。另一方面，现在详细的路网数据可以免费下载，这些数据可能会为网络健壮性评估提供不同的见解。将容量加权特征向量中心度方法应用于大型网络强连通部分和弱连通部分的识别。特征向量中心度是基于网络拓扑的评价方法之一，计算量小。该方法适用于有向网络，不要求其邻接矩阵是对称的。数值算例表明，容量加权特征向量中心度分析可以识别网络的强连通性和弱连通性，并可用于网络连通性的稳健性评估。
The methods to evaluate the robustness of a network have been extensively studied. Such methods often require obtaining traffic equilibrium conditions or solving mathematical problems, and these methods can only be applied to a network of limited size. On the other hand, nowadays detail road network data can be downloaded freely, and such data may provide different insights on network robustness evaluation. This paper applies the capacity-weighted eigenvector centrality method to identify the strongly and weakly connected parts of large networks. The eigenvector centrality is one of the evaluation methods based on network topology with a small computational load. This method can be applied to directed networks and does not require their adjacency matrices to be symmetric. Several numerical examples showed that the capacity-weighted eigenvector centrality analysis can identify the strongly and weakly connected parts of the network, and it can be used to evaluate connectivity of network for robustness.