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A Survey on Air Traffic Control (ATC)

  • Unique Paper ID: 168457
  • Volume: 11
  • Issue: 5
  • PageNo: 977-980
  • Abstract:
  • Modern technology is required for safe and efficient air traffic control systems in this situation. This research evaluates machine learning and artificial intelligence as viable remedies for these problems, elaborating on their co-integration in the ATC application. Air traffic management problems with anomaly identification, pattern recognition, and conflict resolution can all be resolved with the use of AI systems. The suggested approach makes use of XAI technology to increase transparency and ATCO confidence. We use real-time data input from meteorological and aviation sources to our ML models to predict operational hazards related to on-time arrival. Using gradient-boosting algorithms such as XGBoost, with the Shapley Additive explanations technique and LIME, facilitates higher interpretability of the system. It illuminates the potential that AI might bring to ATC systems as far as optimizing flight paths, reducing delay in general, and managing the airspace more efficiently. The results of this research will open up further ground for advancements in traffic management of conventional and UAS aircraft.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 5
  • PageNo: 977-980

A Survey on Air Traffic Control (ATC)

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8.01 (Year 2024)
UGC Approved
Journal no 47859

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