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Addressing Increasing Air Traffic With Efficient ATC Systems

by | Apr 17, 2024 | ATC Systems

The commercial aviation sector isn’t just recovering from the pandemic, it’s booming. Over the next 10 years, demand for commercial flights over the UK is expected to increase by 40%. This presents a major challenge to many existing ATC systems, which are designed for slower rates of change and often struggle with scalability.

To manage this growth safely and efficiently, it is critical for airports to have efficient ATC systems in place. But what are the solutions to this?

AI And Machine Learning

The obvious answer – it being 2024 – is artificial intelligence (AI). AI systems have the capacity to process a far broader range of variables and data streams than traditional coding systems, making the technology, at face value at least, perfectly suited to manage the complexity of UK air traffic control systems. The challenges that make it difficult to scale conventional air traffic control system software, such as the sheer complexity of managing air traffic, diverse aircraft types, fluctuating weather conditions, airspace restrictions, and varying flight paths, would be surmountable given an efficient AI system.

But here’s the catch. So far, AI systems have demonstrated low reliability in fully understanding complex ATC situations, which raises questions about their safety and performance outcomes. As systems become more fully automated, it becomes more necessary to anticipate and mitigate any risks arising from technical failures and malfunctions.

However, the Alan Turing Institute is currently collaborating with NATS, one of the UK’s leading ATC service providers, to incorporate machine learning into improved air traffic control services.

Streamlined Airspace Management

NATS is testing an Automatic Dependent Surveillance Contract Baseline 2 (ADS-CB2) communication system, which has the potential to improve the accuracy of ATC predictions and queue management tools.

Machine learning is an important aspect of AI, but isn’t itself AI. Machine learning is a data processing system, while AI is a control system. The proposed NATS system leverages machine learning to help predict traffic patterns, identify congestion points, and suggest optimal routes for planes, while maintaining overall human control over ATC decisions and support systems.

This collaboration between a leading research organisation such as the Alan Turing Institute (an innovator in software development in general, and AI in particular) and a prominent air navigation service like NATS is a significant step forwards towards integrating modern software into ATC systems, which are often still dependent on legacy infrastructure and technology.

If successful, the move has the potential to streamline air traffic control processes to accommodate fluctuating demand and increased volume, while also increasing safety and sustainability in commercial airspace management.

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