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As demand for air transport continues to grow, the complexity of the operating environment of air traffic has intensified. Air Traffic Controllers (ATCos) are faced with the critical task of making timely decisions in response to rapidly changing air traffic. In this context, maintaining Situation Awareness (SA) becomes critical, directly influencing ATCos’ decision making and preventing potential traffic accidents or incidents. This paper presents a systematic review of the theoretical development of SA measurement techniques for ATCos. Firstly, the measurement techniques developed to assess individual and team SA are discussed and summarized. Additionally, some recently developed novel measurement methods are introduced. Four specific techniques applied to evaluate ATCos’ SA are highlighted. Second, the article analyzes the comprehensive utilization of neurophysiological measurement techniques, with a particular focus on eye movement and EEG methodologies. These techniques exhibit promising potential in measuring ATCos’ SA and are explored in-depth. Furthermore, four distinct types of sensitivity factors that primarily affect ATCos’ SA are summarized. This paper provides some insight into the current state of SA measurement techniques for ATCos. It concludes with recommendations for future research, specifically addressing the evaluation of ATCos SA in high-automation environments.

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.