Management of air traffic during the arrival phase in the terminal management area (TMA) is becoming increasingly complex and can result in very high workloads for air traffic controllers (ATCO), especially when traffic loads become high/very high. This may lead to a loss of efficiency in the management of arrival trajectories, in particular when delivering aircraft to intercept the final approach path / localiser with optimal separation. Consequently, this may impact airport capacity, flight path efficiency, runway throughput, and operational safety during these periods.

As a result, additional capacity issues can propagate across the wider air traffic management (ATM) network, particularly if several airports experience this type of issue at the same time. Moreover, as new airspace users with different performance characteristics to conventional aircraft are integrated into the airport environment, additional capacity/efficiency challenges may manifest themselves, rendering the ATCO task even more difficult and complex.

With these challenges in mind, the ORCI research will investigate a new artificial intelligence (AI) based decision support tool (DST) that can help to enhance ATCO performance in the TMA approach phase, even as traffic demand and complexity becomes very high. This will be achieved by providing ATCO with key information (e.g. “what” distance between consecutive arrivals would be achieved at the ILS localiser interception point “if” the ATCO started vectoring instruction phraseology “right now”) in support of trajectory management.

This is particularly important given the increasing pressure on ATCO communications, where higher traffic levels and operational demands are leading to communication overload. By reducing the need for excessive vectoring communications, ORCI aims to lower the workload on ATCOs, enabling them to manage traffic more efficiently, and focus on critical decision-making, ultimately contributing to an optimal arrival stream at the airport.