TMAs, especially those serving major airport hubs and/or multi-airport systems, are areas of heavy congested traffic. Busy TMAs could benefit from further automation that would improve capacity, flow and trajectory efficiency and safety. The current air traffic control (ATC) paradigm in TMAs consists of having flights and their intentions identified by the ATCOs, supported by a series of information acquisition and analysis tools, such as AMAN (providing a sequence), trajectory predictions, safety nets and instruction adherence monitoring, most of which are integrated into the ATM systems in use. ATCOs assimilate the information available, incorporate other background information, make decisions and instruct the flights. They also interact with the ATM system to keep it up to date with the decisions and the feedback received from the flights.

The ATCO data gathered through this interaction is currently barely used beyond the immediate information update cycles and, possibly, post ops investigations. This wealth of Big Data, together with the introduction of ML algorithms that will learn to predict patterns and ATC instructions can be taken advantage of much more to improve assistance, and corresponding HMI will be developed through TADA and AMAN will benefit from an improvement through the use of the same data and ML.