Journal
RELIABILITY ENGINEERING & SYSTEM SAFETY
Volume 242, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2023.109766
Keywords
Node importance identification; Unweighted urban rail transit network; Adjacency Information Entropy; ABC classification method; Network performance
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Inspired by the theory of degree entropy, this study proposes a new node identification approach called Adjacency Information Entropy (AIE) to identify the importance of nodes in urban rail transit networks (URTN). Through numerical and real-world case studies, it is found that AIE can effectively identify important nodes and facilitate connections among non-adjacent nodes.
Inspiring by the theory of degree entropy, and considering both the location of the evaluated node and its neighboring nodes in an unweighted urban rail transit network (URTN), a new node identification approach called Adjacency Information Entropy (AIE) is applied to identify the importance of node in URTN. An undi-rected and unweighted network, a single-way directed and unweighted network, and a double-way directed and unweighted network are constructed as the background of the numerical study, some other previous approaches are used as the comparison algorithms. Finally, based on the double-way directed and unweighted network topology of Chengdu Metro, a real-world case study is conducted. We find that: (i) For a node in a directed and unweighted network, as long as the in-degree and out-degree of a node are not both 0, then the node can be identified based on AIE. (ii) For a double-way directed and unweighted network, if a node has higher node degree and higher Adjacency Degree, then it is more important in the network. (iii) If a node has high AIE in the entire topology of URTN, then it generates connections among non-adjacent nodes.
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