DOIONLINE 
DOIONLINE Publish In |
International Journal of Electrical, Electronics and Data Communication (IJEEDC)-IJEEDC |
 Journal Home Volume Issue |
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Issue |
Volume-12,Issue-9 ( Sep, 2024 ) | |||||||||
Paper Title |
Artificial Intelligence and Air Traffic Sectorisation | |||||||||
Author Name |
Denis Odić, Damir Džubur, Muharem Šabić | |||||||||
Affilition |
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Pages |
34-40 | |||||||||
Abstract |
Artificial intelligence (AI) is already starting to transform how the world lives and works, and the pace of AI deployment is currently rapidly accelerating. As a sector, aviation and air traffic management (ATM) is ideally placed to take full advantage of AI, in particular machine learning. ATM is powered by air to ground and ground to ground data flows – and „big data‟ is a prerequisite for the successful use of AI. Indeed, AI and machine learning are already contributing to a wide spectrum of value opportunities in the aviation/ ATM industry, from efficiency-focused to safety critical applications. AI has huge potential for use in areas where it can reduce human workload or increase human capabilities in complex scenarios, e.g. to support air traffic controllers (ATCOs), pilots, airport operators, flow controllers or cybersecurity officers. Artificialintelligence(AI) has the power to transform and revolutionize almost any industry in the world; almost no one denies that statement nowadays. Keywords - Artificial intelligence (AI), Air Traffic Management (ATM), Dynamic Airspace Sectorisation (DAS), Machine learning (ML) | |||||||||
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