Determination of Critical Edges in Air Route Network Using Modified Weighted Sum Method and Grey Relational Analysis

• Published in: IEEE transactions on intelligent transportation systems Vol. 24; no. 12; pp. 15578 - 15589
• Main Authors: Ahmad, Amreen, Ahmad, Tanvir, Ahmad, Musheer, Muthanna, Ammar, Gupta, Brij, Abd El-Latif, Ahmed A.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: air route network; Air transportation; Airports; Complex networks; Computational modeling; Critical edges; Resilience; Robustness; salient nodes; Social networking (online); Topology; Transportation networks; Transportation systems
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2022.3200140

The air transportation system has attracted due attention from researchers due to its fast expansion over the last decade. Past research has focused on air transportation networks (ATN), but this work considers the resilience of the air route network. This research work proposes a modified approach based on GRA-WSM, named MA (Modified Approach based on GRA-WSM) for the identification of critical edges that form the backbone of the Chinese air route network. MA is a two-step process: Initially, important nodes are identified using the proposed GRA-WSM, and second, a novel approach is used for the computation of critical edges. Previously, researchers have used edge betweenness centrality measure to identify vital edges. But it took into account the global information of a node. This research work considers different centrality measures as the multi-attribute of the network, to take advantage of each centrality measure. The proposed MA approach aims to minimize the robustness of the network after the removal of some edges and the result is the set of critical edges. The critical edges found by the proposed MA approach are different from the edges that are topologically more important. These findings provide new perspectives on how to better understand other real-world networks.