Study on evolution characteristics of air traffic situation complexity based on complex network theory

2016 ◽  
Vol 58 ◽  
pp. 518-528 ◽  
Author(s):  
Hongyong Wang ◽  
Ziqi Song ◽  
Ruiying Wen ◽  
Yifei Zhao
Author(s):  
Minggong Wu ◽  
Zekun Wang ◽  
Xusheng Gan ◽  
Guozhou Yang ◽  
Xiangxi Wen

The air traffic density in the terminal area is high and the traffic situation is relatively complex by the development of aviation, which brings great challenges to controller. In order to understand the flight situation and provide decision basis for controllers, this paper proposes a key flight conflict nodes identification method based on complex network theory and Analytic Hierarchy Process (AHP)-entropy weight method. Firstly, an aircraft state network is established with aircraft as nodes and Airborne Collision Avoidance System (ACAS) communication relations as connecting edges. On this basis, four parameters, node degree, node weight, clustering coefficient and betweenness, are selected as evaluation indexes of node importance, and the weight of each index is determined by using AHP. And entropy weight method is introduced to revise the results. Node importance is calculated through multi-attribute decision-making method to determine key conflict aircrafts. The simulation and experiment on the artificial network and the aircraft state network of a certain day in the terminal area of Kunming Changshui Airport show that the method proposed in this paper can identify the key flight conflict nodes in the aircraft state network, allocate the selected node deployment can effectively reduce the complexity of the aircraft state network, can provide reference for air traffic control services (ATCS), and reduce the allocation difficulty of controller.


Author(s):  
Shuang Song ◽  
Dawei Xu ◽  
Shanshan Hu ◽  
Mengxi Shi

Habitat destruction and declining ecosystem service levels caused by urban expansion have led to increased ecological risks in cities, and ecological network optimization has become the main way to resolve this contradiction. Here, we used landscape patterns, meteorological and hydrological data as data sources, applied the complex network theory, landscape ecology, and spatial analysis technology, a quantitative analysis of the current state of landscape pattern characteristics in the central district of Harbin was conducted. The minimum cumulative resistance was used to extract the ecological network of the study area. Optimized the ecological network by edge-adding of the complex network theory, compared the optimizing effects of different edge-adding strategies by using robustness analysis, and put forward an effective way to optimize the ecological network of the study area. The results demonstrate that: The ecological patches of Daowai, Xiangfang, Nangang, and other old districts in the study area are small in size, fewer in number, strongly fragmented, with a single external morphology, and high internal porosity. While the ecological patches in the new districts of Songbei, Hulan, and Acheng have a relatively good foundation. And ecological network connectivity in the study area is generally poor, the ecological corridors are relatively sparse and scattered, the connections between various ecological sources of the corridors are not close. Comparing different edge-adding strategies of complex network theory, the low-degree-first strategy has the most outstanding performance in the robustness test. The low-degree-first strategy was used to optimize the ecological network of the study area, 43 ecological corridors are added. After the optimization, the large and the small ecological corridors are evenly distributed to form a complete network, the optimized ecological network will be significantly more connected, resilient, and resistant to interference, the ecological flow transmission will be more efficient.


2014 ◽  
Vol 13 (5) ◽  
pp. 963
Author(s):  
Burgert A. Senekal ◽  
Karlien Stemmet

The theory of complex systems has gained significant ground in recent years, and with it, complex network theory has become an essential approach to complex systems. This study follows international trends in examining the interlocking South African bank director network using social network analysis (SNA), which is shown to be a highly connected social network that has ties to many South African industries, including healthcare, mining, and education. The most highly connected directors and companies are identified, along with those that are most central to the network, and those that serve important bridging functions in facilitating network coherence. As this study is exploratory, numerous suggestions are also made for further research.


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