Evaluating the effectiveness of the trajectory prediction aids on air traffic controller in the context of workload and situation awareness

Abstract

Due to the increase of air traffic volume forecasted for the future, air traffic controllers (ATCOs) are expected to face unprecedented problems and situations. Hence, technology, such as automation, has been extensively explored to help ATCOs better manage air traffic to ensure high levels of safety in Air Traffic Control (ATC). In recent years, various trajectory prediction aids have been widely investigated as beneficial automation tools to help controllers by providing predicted information. They are not yet available in the current ATC workspace as more examinations are required. Past experiments are limited, as experiments have been mostly conducted with only one particular aid or as a comparison between two aids. In addition, insufficient researches have been done that provide ATCOs with the freedom of choice in using a variety of aids. Thus, this experiment was conducted to evaluate the usefulness of trajectory prediction aids on ATCOs in the context of workload and situation awareness, when they were given the freedom of choice. As such, they would have the autonomy to choose the particular aid that would be useful to them in better managing the air traffic. Plan view, vertical view, speed view and rate of climb/descent view were the four different types of the trajectory prediction aids introduced in the experiment. The results of this experiment indicate the usefulness of trajectory prediction aids in helping ATCOs to meet future demands in terms of situation awareness and workload. This can be seen from the reduction in percentage of timeouts and probe response latency, which implied lower workload and higher situation awareness. This proved that automation has the potential to reduce workload and improve situation awareness of ATCOs. Also, the experiment has preliminarily proven that the freedom of choice in using the automation aids is beneficial. This might be attributed by the diverse abilities of ATCOs and therefore the need to adopt different tools in optimising their performance. Future studies could focus on better training, parameter selections and display interfaces to achieve more precise and comprehensive results.