Harnessing the Power of Artificial Intelligence for Air Traffic Management

Aerospace and Defense Review | Thursday, September 07, 2023

AI should be able to assist ATM actors in meeting the problems of lowering CO2 emissions and those connected to the complexity of air traffic (such as traffic density and new entrants).

Fremont, CA: AI is currently widespread and interwoven into many aspects of our everyday lives. The aviation sector also significantly impacts the integration of and advantages of this new technology.

Prior to the pandemic, there has been a tremendous and ongoing rise in air traffic, which has sparked intense research into using AI in the aeronautical area, particularly in air traffic management. AI should be able to assist ATM actors in meeting the problems of lowering CO2 emissions and those connected to the complexity of air traffic (such as traffic density and new entrants). Despite this, fundamental challenges about the law, safety, and security get raised by this new technology.COVID-19's reduction in traffic should enable aviation stakeholders to use the ensuing years to get through these genuine challenges.

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The following areas are potential focal points for AI:

Demand forecasting

The accuracy and predictability of traffic demand might increase by supplementing the present computational techniques with AI algorithms based on the real flight plan and trajectory data. This improves the effectiveness of demand and capacity balance (DCB).

Optimal sectorization

AI has a crucial role to play in managing dynamic airspace. Furthermore, the use of AI in this field can enable the handling of complexity and volumes of data that are beyond the capability of humans. In order to enhance airspace design, redefined sector borders that better represent traffic evolution are suggested to give an appropriate dynamic configuration of sectors while considering traffic evolution in various locations.

Optimization of flight plans

AI algorithms can suggest the best flight paths for airplanes and help lower greenhouse gas emissions by combining the many flight plans, traffic data, weather, etc.

4D Trajectory

AI helps to better predict the "date to flight" by combining take-off information and airport activities (delays, connections), as well as to support decision-making on specific operations, such as the go-around.

Remote Towers

AI for pattern recognition can identify parking spots and holding areas and notify the controller of certain conditions.

Via various tools and methods, the application of AI may be a crucial facilitator to raising the safety and effectiveness of services offered by ANSPs.

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