Open AccessA measure of identifying influential waypoints in air route networks
Author(s) -
Guangjian Ren,
Jiasong Zhu,
LU Chao-yang
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0203388
Subject(s) - betweenness centrality , closeness , waypoint , computer science , centrality , entropy (arrow of time) , aggregate (composite) , data mining , computational biology , biology , mathematics , statistics , real time computing , physics , mathematical analysis , quantum mechanics , materials science , composite material
As the basic carrier of air flight operation, air route network (ARN) is of great significance to the smooth operation of flights. However, the waypoint is a core part of the route, so it is an important topic to identify influential waypoints in ARN. In this paper, a method to identify the influence of the node in ARN based on an improved entropy weight (IEW) method is proposed. Then, centrality measures including degree, closeness, betweenness and eigenvector as the multi-attribute of ARN in IEW application. IEW method is used to aggregate the multi-attribute to obtain the evaluation of the influence of each waypoint. To demonstrate the effectiveness of the IEW method, three real ARNs are selected to conduct several experiments with susceptible infected recovered (SIR) model. The results show the efficiency and practicability of the proposed method.