TRANSFORMING AIR TRAFFIC MANAGEMENT WITH BIG DATA AND ARTIFICIAL INTELLIGENCE

The integration of Big Data and Artificial Intelligence (AI) has the potential to significantly transform air traffic management by offering innovative solutions to enhance safety, efficiency, and capacity of operations. With the growing demand for air travel and the increasing complexity of airport operations, these emerging technologies are becoming essential to tackle industry challenges and prepare aviation for the future. The main advantage of this integration is the improvement in decision-making, with the collection and analysis of large volumes of real-time data, such as weather information and flight data, allowing for the prediction of traffic patterns and identification of congestion. Furthermore, AI can predict and prevent failures in critical systems, such as radar and communication systems, enabling corrective actions before failures occur. The optimization of resource usage, such as flight routes and takeoff and landing operations, is also facilitated, increasing airport capacity and reducing wait times and delays. Safety is also enhanced, with the detection of hazardous flight conditions and more efficient coordination among airlines, controllers, and emergency services. However, the implementation of AI and Big Data faces challenges, such as the need for robust infrastructure, privacy protection, and the adaptation of industry professionals. Investing in secure technologies and properly training operators are key steps to fully leverage the potential of these innovations in air traffic management.

Authors

Publication Details

Journal
International Seven Journal of Multidisciplinary
Published
2024-12-03
DOI
https://doi.org/10.56238/isevmjv1n2-009
Citations
1
Primary Topic
Air Traffic Management and Optimization
Type
article
Field-Weighted Citation Impact
1.30
Controls
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article

TRANSFORMING AIR TRAFFIC MANAGEMENT WITH BIG DATA AND ARTIFICIAL INTELLIGENCE

André Luiz Macedo da Cruz
1 citations
International Seven Journal of Multidisciplinary
Air Traffic Management and Optimization
1.30
article

TRANSFORMING AIR TRAFFIC MANAGEMENT WITH BIG DATA AND ARTIFICIAL INTELLIGENCE

André Luiz Macedo da Cruz
article en
1 citations

Abstract

The integration of Big Data and Artificial Intelligence (AI) has the potential to significantly transform air traffic management by offering innovative solutions to enhance safety, efficiency, and capacity of operations. With the growing demand for air travel and the increasing complexity of airport operations, these emerging technologies are becoming essential to tackle industry challenges and prepare aviation for the future. The main advantage of this integration is the improvement in decision-making, with the collection and analysis of large volumes of real-time data, such as weather information and flight data, allowing for the prediction of traffic patterns and identification of congestion. Furthermore, AI can predict and prevent failures in critical systems, such as radar and communication systems, enabling corrective actions before failures occur. The optimization of resource usage, such as flight routes and takeoff and landing operations, is also facilitated, increasing airport capacity and reducing wait times and delays. Safety is also enhanced, with the detection of hazardous flight conditions and more efficient coordination among airlines, controllers, and emergency services. However, the implementation of AI and Big Data faces challenges, such as the need for robust infrastructure, privacy protection, and the adaptation of industry professionals. Investing in secure technologies and properly training operators are key steps to fully leverage the potential of these innovations in air traffic management.

International Seven Journal of MultidisciplinaryVol. 1(2)
Industry, innovation and infrastructure
Openalex Percentile: Top 11%
Air Traffic Management and Optimization
1.30
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TRANSFORMING AIR TRAFFIC MANAGEMENT WITH BIG DATA AND ARTIFICIAL INTELLIGENCE — André Luiz Macedo da Cruz · International Seven Journal of Multidisciplinary (2024) | TGRS Research Map | TGRS