Graph-based Complexity Forecasts in UK En Route Airspace Using Relevant Aircraft Interactions
arXiv:2605.23696v1 Announce Type: new Abstract: Effectively managing Air Traffic Control Officer (ATCO) workload is crucial in maintaining operational safety. Group supervisors use tools that estimate upcoming traffic load to aid decision-making. However, industry-standard models can fail to capture the nuances of upcoming air traffic complexity. This study presents a probabilistic approach to forecast the complexity of an airspace sector using the number of relevant aircraft pairs, i.e., those that require monitoring or deconfliction by a controller, as a proxy measure for ATCO workload. We a
The increasing complexity of air traffic and advancements in AI/graph-based modeling allow for more sophisticated solutions to long-standing air traffic management challenges.
Improved air traffic control (ATC) workload management can enhance safety, increase airspace capacity, and reduce delays, directly impacting global logistics and travel efficiency.
The development of more accurate, probabilistic forecasting models for air traffic complexity offers the potential to move beyond current industry-standard tools, leading to more proactive ATCO decision-making.
- · Air Traffic Control organizations
- · Aviation software developers
- · Airline industry
- · AI/ML research institutions
- · Providers of legacy air traffic management tools
- · Airports with limited expansion capabilities
More efficient air traffic flow and reduced ATCO stress.
Increased acceptance and integration of AI-driven tools in critical infrastructure management.
Potential for fully autonomous or highly augmented air traffic control systems in the long term.
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Read at arXiv cs.LG