Abstract
In an airway network, some critical links exist that are vital for the structural integrity and performance of the network. The detection of such links may assist with improving the imbalance between the limited airspace capacity and the ever-increasing traffic demand, which elicit flight delays, significant economic losses, etc. However, it is challenging to identify such links as they evolve (both in space and time) with changing traffic flow dynamics. This paper proposes a complex network approach for spatial-temporal critical links detection in a given airway network. First, flight track data is employed to characterize the airway network as weighted spatial-temporal networks. Then edge centrality and network percolation metrics are adopted to detect the critical links in each snapshot of the spatial-temporal networks. Afterward, the critical links detected by the two metrics are spatially overlapped to determine the final critical links over time. To examine the operational validity of the proposed method, we carry out a case study on the Southeast Asia airway network derived from one-month flight track data. Results demonstrate that the spatial distribution of the critical links varies over different traffic scenarios, and most of the identified critical links are found in the transition sectors with complex traffic situations. Four links, which are parts and at crosses of major trunk airways connecting to major navigation aids (VOR/DME) in the studied network, appear highly in all examined traffic scenarios. The unavailability of such links may lead to traffic flow disruptions. Observations by subject matter experts from air-traffic data visualizations demonstrate that the complex network-based methods can dynamically identify airway links that are operationally critical under time-evolving air-traffic scenarios. With good traffic flow prediction tools in the future, this method can be adopted to predict critical links in airway networks to better assist controllers in real-time air traffic management.
Highlights
A Lthough the air traffic demand during the outbreak of the COVID-19 pandemic almost came to a standstill, the traffic demand is on its way to ramping up across nations as many traveling restrictions are lifted [1]
Note that identifying critical links in an airway network can assist with air traffic flow management, flight scheduling, and resource allocation
In order to quantify how critical a link of an airway network is, two metrics were introduced, i.e., edge betweenness centrality and percolation theory
Summary
A Lthough the air traffic demand during the outbreak of the COVID-19 pandemic almost came to a standstill, the traffic demand is on its way to ramping up across nations as many traveling restrictions are lifted [1]. The biggest challenge confronted by air navigation service providers (ANSPs) is the capacity-demand dilemma, as known as the imbalance between airspace capacity and traffic demand [3], [4], which is the major source for en-route congestion that elicits traffic delays, and environmental impact [5], [6]. An airway network constitutes the virtual highway in the sky on which the air traffic operates. It is very promising to manage congestion by improving air traffic flow on airway networks [7], [8]
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