It is an area of computer science that includes highly advanced computational methods that mimic the way the human brain works, and is a powerful technology that can find patterns in massive unstructured data sets and improve its own performance as more data becomes available.
Today, typical AI capabilities include speech, image and video recognition, smart automation, advanced simulation, as well as complex analytics and predictions.
The application of AI in the aviation field can help to overcome the current challenges of capacity, environment, connectivity and security.
As highlighted in CANSO Europe Vision 2035, CANSO believes it is paramount to find a clear journey towards system-centric technology, increased automation, oriented architecture, virtualisation and interoperability to transform air traffic management (ATM) performance. Digital transformation of the aviation industry enables data-driven technologies like AI which form a critical part of the highperformance ATM systems of the future.
CANSO is focused on building a stronger future today – and some member air navigation service providers (ANSPs) are already actively harnessing the power of AI to improve current processes, and create new ATM applications.
For example, DSNA in France is developing new AI tools to advance operational solutions to deploy the optimal configuration of sectors, and thus to optimise capacity with available resources. European airspace is divided into several elementary blocks of airspace which allow modularity and flexibility in building the different airspace configurations to meet expected and effective traffic flows. In the case of France, its airspace is composed of 168 elementary sectors.
In the tactical phase, Supervisors and Flow Management Positions (FMPs) analyse, on a continuous basis, traffic demand and available capacity from the analysis of the operational air traffic control centre (ACC) data, including resources, technical availability, unexpected events and environmental data (weather, military activity). With their expertise, they manually determine the most adequate sector configurations. Without automation, identifying the sequence of adequate ACC sector configurations according to traffic flows and associated workload is time-consuming. And only solutions from predefined catalogues are routinely used, limiting the possibilities.
DSNA is therefore using innovative techniques, offering dynamicity, optimisation and time-saving in capacity management and ACC sector configuration. In order to maximise the benefits of airspace modularity while optimising resources, automated functions have been developed to support Supervisor and FMP decision-making. A new AI tool is able to optimise airspace solutions in case of high workload and unforeseen events which could require quick decisions for airspace re-organisation. It also provides additional means in the Air Traffic Flow Control Management (ATFCM) toolkit using dynamic sector configurations to facilitate users' preferred routings.
In the UK, NATS is using AI to measure air traffic characteristics (for example, high traffic volume, weather conditions) and predict the likelihood of potential safety events, like runway exit points for example. This will enable strategic corrective actions to avoid them. NATS is also exploring how AI could be used to help reduce flight delays. A project is now underway, within NATS’ bespoke Digital Tower Laboratory at Heathrow Airport, to test whether a combination of ultra HD 4K cameras along with state-of-the-art AI and machine-learning technology can be used to help improve the airport’s landing capacity in times of low visibility and improve punctuality (see NATS article in this issue). Non-operational trials are now underway to understand the feasibility of introducing the technology into service as early as this year.