Figure 1 - uploaded by Gijs de Rooij
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Simulator interface, with blue aircraft allocated to automation and green aircraft to the human ATCO. Background colors have been inverted here for clarity.
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Allocation is a challenge for higher levels of automation in air traffic control, where flights can be dynamically assigned to either a human or an automated agent. Through an exploratory experiment with six professional air traffic controllers, insight was gained into the possibilities and challenges of human-automation teamwork in an en-route env...
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... professional ATCOs (age M = 38.3, SD = 10.0, years of experience M = 14.8, SD = 8.7), from Maastricht Upper Area Control (MUAC) participated in a real-time simulator experiment. A TU Delft-built Java-based simulator ( Fig. 1) was designed to mimic the MUAC interface, to ensure that participants could focus on working with the experimental automation. A 1920 x 1920 pixels 27" display was used with a standard computer mouse for control inputs. The ATCO could delegate a flight to automation by pressing "ASSUME TO AUTO". Once the flight was assumed, a ...
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In 2019, China's air traffic control service handled approximately 10.766 million flight movements, with an increase of 7.60% compared to 2018. However, this rapid growth has resulted in flight delays due to airspace capacity limitations, and air traffic control complications. Faced with the realities of airspace capacity limitations, Air Traffic F...
Citations
... The human ATCO is then responsible for controlling the remaining traffic with active involvement. Exploratory research showed that such a shared airspace is feasible and accepted by ATCOs under certain conditions [4]. Assigning flights to either a human or a computer agent can be regarded as the next evolutionary phase in Flight-Centric ATC, a concept where specific flights are assigned to different human ATCOs [5]. ...
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.
... Thus, investigations should be performed on whether the same vigilance improvement effects can be obtained in ways more compatible with current ATC work environments. De Rooij et al. [18] proposes automating only part of air traffic, with part of them still requiring conventional manual control. This might have the same effect as fictional aircraft, but without introducing new elements to the work environment. ...
The introduction of more advanced automation in air traffic control seems inevitable. Air traffic controllers will then take the role of automation supervisors, a role which is generally unsuitable for humans. Gamification, the use of game elements in non-gaming contexts, shows promising results in mitigating the effects of boredom in highly automated domains requiring human supervision. An example is luggage screening, where dangerous items are rarely found, through projecting fictional threats on top of luggage scans. This paper presents and experimentally tests a proposed implementation of gamification within a highly-automated en-route air traffic control work environment. Fictional flights were superimposed among automatically controlled real traffic, thus creating fictional conflicts that needed resolving. System supervisors were given the task of supervising the behaviour of a fully automated conflict detection and resolution system, while routing fictional flights safely and efficiently through the sector, avoiding conflicts with other flights (both real and fictional). Automation anomalies were simulated during the experiment, as well as an automation failure event, after which the system supervisor needed to assume manual control over all traffic. The presence of fictional flights increased reported concentration levels among participants and improved supervisory control performance. However, some participants reported that fictional flights were distracting. Thus, while the use of fictional flights increases engagement, it might negatively affect other cognitive functions, and with that, compromise safety. Further research is recommended involving professional air traffic controllers, improved measurement tools and a longitudinal study that better excites boredom, complacency and skill erosion.
... In a previous work [13], we experimentally tested a preliminary setup where ATCOs could delegate individual flights to an automated system. While it showed the feasibility of such a shared airspace and its acceptance among ATCOs, it also revealed that ATCOs adopted different allocation strategies than we had anticipated. ...
... By focusing on MUAC, the tasks are linked to their currently operational (interface) tools. Expanding upon our work in [13], we discuss the expected impact of delegating flights and potential mitigation measures inspired by current procedures and tools. The next step will be to validate and objectively quantify the models in a follow-up experiment, briefly described in Section IV. ...
... 1) The ATCO has to manually resolve the conflict, or delegate the flight to make it a fully computer-directed conflict [13]. ...
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 between a human operator and an automated system, with specific flights fully delegated to automation, operators can maintain their skills and stay actively involved in controlling the rest of the traffic. This does, however, lead to new forms of mixed conflicts, where two flights are controlled by different agents. A smart flight allocation strategy, starting with the delegation of basic flights requiring little monitoring or cognitive effort, is expected to improve combined human-automation performance. In this paper, we present flowcharts to model en-route air traffic controller cognitive think and action processes in two core tasks: conflict detection and resolution. We qualitatively describe the impact of delegating flights to automation and the associated introduction of mixed conflicts. Once empirically validated and quantified in follow-up research, these models can be used to design flight allocation strategies for future human-automation teams.
