Browsing by Author "Kurauchi, Fumitaka"

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Connectivity evaluation of large road network by capacity-weighted eigenvector centrality analysis
(1970-01-01)
Ando, Hiroe
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Bell, Michael
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Kurauchi, Fumitaka
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Wong, K. I.
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Cheung, Kam-Fung
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.
Investigating transport network vulnerability by capacity weighted spectral analysis
(2017-05-01)
Bell, Michael G. H.
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Kurauchi, Fumitaka
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Perera, Supun
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Wong, Walter
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運輸與物流管理系註：原交通所+運管所
Transport networks operating at or near capacity are vulnerable to disruptions, so flow bottlenecks are potent sources of vulnerability. This paper presents an efficient method for finding transport network cuts, which may constitute such bottlenecks. Methods for assessing network vulnerability found in the literature require origin-destination demands and path assignment. However, in transport network planning and design, demand information is often missing, out of date, partial or inaccurate. Capacity weighted spectral partitioning is proposed to identify potential flow bottlenecks in the network, without reference to demand information or path assignments. This method identifies the network cut with least capacity, taking into account the relative sizes of the sub-networks either side of the cut. Spectral analysis has the added advantage of tractability, even for large networks, as shown by numerical examples for a five-node illustrative example, the Sioux Falls road network and the Gifu Prefecture road network. (C) 2017 Elsevier Ltd. All rights reserved.