Enhancing Air Traffic Management: The Transformative Role of Artificial Intelligence in Modern Air Traffic Control

Authors:
Desiya Nanban, Jennifer Selvan, A.T. Ashmi Christus, Muhammad Al Amin

Addresses:
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India. Department of Electronics and Communication Engineering, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India. Department of International Relations, Sichuan University, Chengdu, China. dn4939@srmist.edu.in, js6485@srmist.edu.in, ashmichristus@dhaanishcollege.in, alamin2022@stu.scu.edu.cn

Abstract:

Since the Wright brothers' December 17, 1903 flight, the aviation business has grown quickly alongside IT. Growth is concentrated in aircraft development, airport infrastructure, and air traffic control. AI will revolutionize each of these fields. AI optimizes fuel usage, structural designs, and avionics in aircraft development, making them more efficient and modern. AI streamlines airport check-in, luggage processing, and airport security, improving efficiency and passenger experience. The heart of aviation, ATC, coordinates take-offs, landings, and en-route traffic through Aerodrome Control, Approach Control, and Area Control. ATC may use AI to optimize air traffic management, automate mundane jobs, analyze data for decision-making, and predict traffic flow to avoid congestion and delays. Due to the complexity of air traffic and the requirement for quick human judgment, replacing human ATC operators with AI is difficult. However, AI can be gradually introduced into particular operations to improve efficiency and safety. AI will support these functions as technology advances, making air travel safer and more efficient.

Keywords: AI in Aviation; ATC Units; Decision Support Systems; Air Traffic Management; Automate Mundane Jobs; Air Travel Safer; Technology Advances; Aviation Industry.

Received on: 29/12/2023, Revised on: 03/02/2024, Accepted on: 27/03/2024, Published on: 07/06/2024

DOI: 10.69888/FTSIN.2024.000210

FMDB Transactions on Sustainable Intelligent Networks, 2024 Vol. 1 No. 2, Pages: 72-84

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