2022IEEE Transactions on Intelligent Transportation SystemsRequires access

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

Amreen Ahmad, Tanvir Ahmad, Musheer Ahmad, Ammar Muthanna, Brij B. Gupta, Ahmed A. Abd El‐Latif

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Abstract

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.

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What this paper is about

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.

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Available abstract

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.

Key concepts: Betweenness centrality, Centrality, Robustness (evolution), Network controllability, Computer science, Network science, Katz centrality, Data mining

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