Abstract
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.
Highlights
The airplane currently does not follow a smooth and optimized trajectory
The results show that the DS theory is effective, the DS centrality is more focused on unweighted networks, lacking universality
The basic topological properties of Beijing, Shanghai and Guangzhou air route networks are analyzed by the complex network theory
Summary
The airplane currently does not follow a smooth and optimized trajectory. Yet, they should follow a path on a predefined grid whose nodes are called navigation points or waypoints. Hybrid degree centrality (HC) are used to analyze the importance of nodes in weighted networks [9], and this theory improves the stability of the results to some extent, but ignores the local structure of neighbors. The comprehensive centrality measure based on the Dempster-Shafer (DS) evidence theory is proposed in [10] and [11] to identify influential nodes.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
