Analytical Studies of Artificial Intelligence Based Techniques For Air Traffic Control

Air transport is a complex industry. When most industries gather together they are faced with a problem of growth creation. In addition, air transport is now facing a number of challenges resulting from this growth and the term "sustainability" is the subject of many debates. Over the years the air transport industry became used to the luxury of gaining more and more passengers and freight all over the world. The trouble free flow of customers resulted in perceptions amongst certain consumers of a reduction in the quality of service. It is time again to analyze passengers' needs, in particular after the wake-up call aviation and other transport modes . Considering the recent situation in aviation, this requirement is very hard to fulfil, especially because of rising delays and congested airspace and airports.

Air traffic control is a crucial aspect of aviation safety, ensuring that aircraft operate safely and efficiently in the airspace. With the advent of artificial intelligence (AI) and machine learning (ML) techniques, there has been a growing interest in exploring their potential to improve air traffic control systems. Analytical studies of AI-based techniques for air traffic control involve investigating the use of AI and ML in developing more advanced air traffic control systems.

These studies typically involve a range of analytical techniques, such as simulation models, statistical analysis, and optimization methods, to evaluate the performance of AI-based air traffic control systems. They may also involve the development of new algorithms and techniques that can be applied to real-world air traffic control scenarios.

Some of the specific areas of focus in analytical studies of AI-based techniques for air traffic control may include predicting traffic flow, improving communication and coordination between air traffic controllers and pilots, and optimizing routes and trajectories for aircraft. Other areas of interest may include identifying and mitigating potential safety risks and enhancing situational awareness for air traffic controllers.

Overall, analytical studies of AI-based techniques for air traffic control aim to improve the efficiency, safety, and effectiveness of air traffic control systems using the latest advances in AI and ML. The insights gained from these studies can help inform the development of new air traffic control technologies and strategies that benefit both the aviation industry and passengers.