The next generation Air Traffic Management systems are pushed more and more towards digitization, fueled by the demands to increase capacity and cost-efficiency while also increasing the already high safety and resilience levels. SafeOPS proposed and investigated a decision support tool for Air Traffic Controllers, which provides real time, predictive risk information on the likelihood of approaches to perform a go-around. The underlying idea is that Air Traffic Controllers incorporate this predictive information in the handling of departures and approaches, especially in high traffic situations.
As of today, Air Traffic Controllers recognize the onset of go-arounds through pilots' communication and from observing the aircraft visually or via radar. Go-arounds are a standard and well-established flight procedure, for flight crews as well as for Air Traffic Controllers, despite the relatively low likelihood. Strategies to handle go-arounds however are of reactive nature, meaning they become active once the Air traffic Controller identifies the ongoing go-around. Especially in high traffic congestions, handling a go-around becomes complex, since knock-on effects like separation infringement or wake turbulence challenges with preceding departures can arise. Air Traffic Controllers are trained for such situations, nevertheless, resolving such situations, while maintaining safe separation, increases the workload of the Air Traffic Controller, as well as the Flight Crew.
The idea when providing predictive risk information in this scenario is to thereby improve the situational awareness and thus the decision-making of Air Traffic Controllers, by enabling a proactive approach in handling go-arounds. This shift from reactive to proactive go-around handling could avoid the described knock-on effects, possibly triggered by go-arounds, yielding a positive impact on safety and resilience but also capacity of Air Traffic Management.
The question addressed by SafeOPS was, how predictive tools, and the inherently probabilistic nature of their outputs, will change the approach and departure handling of Tower Controllers. Can predictive tools increase the safety and cost-efficiency and can the resilience of the system be maintained or further improved.
The main objectives of this project towards answering these questions were, in the scope of the proposed go-around handling context, to:
1. develop an AI/ML tool for go-around predictions and explore it in terms of achievable performance metrics as well as explainability,
2. enhance a risk assessment method, such that it can cope with the introduced AI/ML component, and
3. investigate the AI/ML based decision support solution for ATM, and evaluate the effects on capacity, safety and resilience of the ATM operation.
In conclusion, SafeOPS found that these proactive solutions benefit the safety and resilience of the ATM system in complex go-around situations, by providing the Air Traffic Controllers with more time and better information for the necessary coordinative actions, which have to be taken in the event of a go-around. On the contrary, the proactive tactics can reduce capacity/efficiency, in case of false prediction, but only in an amount, which is negligible, compared to the foreseen overall increase of capacity. Thus, predictive risk information can be used as a decision support between a reactive and proactive approach to handle go-arounds, especially in high traffic situations.
