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![Connections between processes, adapted from [15]. For color coding, see Table II.](profile/Gijs-De-Rooij-2/publication/366431358/figure/fig1/AS:11431281109081757@1671710909403/Connections-between-processes-adapted-from-15-For-color-coding-see-Table-II.png)
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![Fig. 1. Connections between processes, adapted from [15]. For color...](profile/Gijs-De-Rooij-2/publication/366431358/figure/fig1/AS:11431281109081757@1671710909403/Connections-between-processes-adapted-from-15-For-color-coding-see-Table-II_Q320.jpg)
In the quest for more efficient air traffic management, a common approach is to allocate an increasing amount of functionality to higher levels of automation, with a supervisory role for humans. This potentially leads to forthcoming issues such as skill degradation and out-of-the-loop phenomenon. If the traffic in an airspace is instead shared betw...
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... the new ATCO has assumed responsibility, they enter a monitoring process that continues for the remainder of the shift. Monitoring involves updating the mental picture and sector plan, and in turn triggers all of the other processes as visualized in Figure 1. While the use of flow charts may suggest purely linear processes, constant attention switching means that the processes can be interrupted or resumed due to shifting priorities. ...
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... To cope with complexity, ATCOs typically make pair-wise comparisons between flights in a hierarchical manner [14]. For example, to detect conflicts, they first scan the flight labels to detect overlapping altitudes, then narrow down the search to flights with crossing trajectories, followed by anticipating their CPA [15]. As such, the ATCOs' strategies, skills and expertise are expected to play a role in how complexity is perceived. ...
... By focusing on these unanimously labeled flights the results can be considered on the conservative side, but it is inevitable to avoid highly personalized results. The relative importance of the CPA and the presence of an altitude overlap that was found in the included flight analysis, strengthens the hierarchical task analysis presented in [15]. As expected, ATCOs seem to predominantly filter flight pairs based on these two characteristics. ...
To alleviate the workload of air traffic controllers, part of the air traffic may be handled by a future automated system. When deciding which flights to delegate, a distinction can be made between basic and non-basic flights, with the former being prime candidates for delegation. The human controller can then focus on the non-basic flights, where human competencies are most valuable and more difficult to automate. The classification of flights is preferably based on objective measures relating to the traffic situation. Existing complexity models are, however, often used for capacity predictions or airspace restructuring and primarily to assess the complexity of a sector as a whole. In this paper we use empirically collected flight complexity ratings from 15 professional en-route air traffic controllers. They indicated which other flights contributed to their complexity assessment of a single flight of interest. This exploratory study was able to build a machine-learning model which adequately classifies these flights, based on a qualified majority of controllers. By analyzing the interactions between the included flights, we discuss whether a classification model can differentiate between basic and non-basic flights, and which traffic features play the largest role. Once this can be done reliably and an appropriate complexity threshold has been chosen, a model can be developed as a starting point for an automatic allocation algorithm that distributes flights between a human controller and the computer.
