Analysis of topological characteristics in air traffic situation networks

Air traffic systems are of great significance to our society. However, air traffic systems are extremely complicated since an air traffic system encompasses many components which could evolve over time. It is therefore challenging to analyze the evolution dynamics of air traffic systems. In this paper we propose a graph perspective to trace the spatial-temporal evolutions of air traffic systems. Different to existing studies which are model-driven and only focus on certain properties of an air traffic system, in this paper we propose a data-driven perspective and analyze a couple of properties of an air traffic system. Specifically, we model air traffic systems with both unweighted and weighted graphs with respect to real-world traffic data. We then analyze the evolution dynamics of the constructed graphs in terms of nodal degrees, degree distributions, traffic delays, causality between graph structures and traffic delays, and system resilience under airport failures. To validate the effectiveness of the proposed approach, a case study on the American air traffic systems with respect to 12-month traffic data is carried out. It is found that the structures and traffic mobilities of the American air traffic systems do not evolve significantly over time, which leads to the stable distributions of the traffic delays as evidenced by a causality analysis. It is further found that the American

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“…An earlier work dealing with network topologies with air traffic system evolution can be found in Ref. [22].…”

Section: Introductionmentioning

confidence: 99%

Tracing the spatial‐temporal evolution dynamics of air traffic systems using graph theories

Xiao

Gao

et al. 2022

Int J of Intelligent Sys

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“…This has laid a foundation for the study of air traffic complexity. After that, WANG applied the complex network theory to the research of air traffic complexity [2], [3], and ATF complexity [4]. However, these researches mainly focus on the overall situation of airspace, and the relationship between agents behavior and the airspace situation is relatively few.…”

Section: Introductionmentioning

confidence: 99%

Identification of Key Nodes in Aircraft State Network Based on Complex Network Theory

Wang

Wen

2019

IEEE Access

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With the development of aviation, the air traffic density in the terminal area is high and the traffic situation is relatively complex, which brings challenges to the flight deployment. In order to fully understand the air flight situation and provide decision-making basis for controllers, this paper proposes a key conflict aircraft identification method based on complex network theory and node deletion method. First, an aircraft state network is constructed with an aircraft as nodes and airborne collision avoidance system (ACAS) communication relations as edges. Network efficiency, network robustness, connection density, and largest component were used as the indexes of network performance. The weight of each index is determined by using AHP-entropy weight method. A multi-attribute decision-making method was introduced to quantify network performance. Then we used a node deletion method to determine key conflict aircrafts. The simulation and experiment are respectively carried out on the artificial network and the aircraft state network of a certain day in the terminal area of Kunming Changshui Airport. The results show that the method proposed in this paper can identify the key conflict points in the aircraft state network. The deployment of selected nodes can not only effectively reduce the complexity of the flight state network, but also provide a reference for air traffic control services and reduce the control difficulty of the controller. INDEX TERMS Aircraft state network, complex network, node deletion, air traffic control (ATC).

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“…From the microscopic perspective, aircraft are considered as nodes, while between-aircraft conflict or proximity relationships are regarded as edges. This helps to explore traffic behavioral characteristics, including flight conflict pattern and local congestion, providing assistance for air traffic control services, and it is focused on traffic flow characteristics [21][22][23]. Based on the above analyses, use of the complex network theory offers some meaningful conclusions on airspace structure and traffic flow characteristics.…”

Section: Journal Of Advanced Transportationmentioning

confidence: 99%

“…Thus, an air traffic situation is essentially a time-evolving complex system. With aircraft as nodes and between-aircraft interrelationships as edges, an undirected air traffic situation network model was built, and then its basic characteristics were statistically analyzed [21,23]. In this study, with aircraft, key waypoints, and route segments as nodes, between-aircraft, aircraft-keypoint, and aircraft-segment complexity relationships as edges, and the intensity of various complexity relationships as weights, we built a dynamic weighted network model and used it to more comprehensively characterize the structure of air traffic situations.…”

Section: Network Complexity Modelmentioning

confidence: 99%

Modeling Air Traffic Situation Complexity with a Dynamic Weighted Network Approach

Wang

Song

Ruiying

2018

Journal of Advanced Transportation

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In order to address the flight delays and risks associated with the forecasted increase in air traffic, there is a need to increase the capacity of air traffic management systems. This should be based on objective measurements of traffic situation complexity. In current air traffic complexity research, no simple means is available to integrate airspace and traffic flow characteristics. In this paper, we propose a new approach for the measurement of air traffic situation complexity. This approach considers the effects of both airspace and traffic flow and objectively quantifies air traffic situation complexity. Considering the aircraft, waypoints, and airways as nodes, and the complexity relationships among these nodes as edges, a dynamic weighted network is constructed. Air traffic situation complexity is defined as the sum of the weights of all edges in the network, and the relationships of complexity with some commonly used indices are statistically analyzed. The results indicate that the new complexity index is more accurate than traffic count and reflects the number of trajectory changes as well as the high-risk situations. Additionally, analysis of potential applications reveals that this new index contributes to achieving complexity-based management, which represents an efficient method for increasing airspace system capacity.

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Analysis of topological characteristics in air traffic situation networks

Cited by 14 publications

References 9 publications

Tracing the spatial‐temporal evolution dynamics of air traffic systems using graph theories

Tracing the spatial‐temporal evolution dynamics of air traffic systems using graph theories

Identification of Key Nodes in Aircraft State Network Based on Complex Network Theory

Modeling Air Traffic Situation Complexity with a Dynamic Weighted Network Approach

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