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Machine learning based air traffic control strategy

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Abstract

Development of an absolute, secure and reliable air traffic control (ATC) has become a challenging problem due to its inherited complexity, emergence of new technologies and growth of airways in the airspace. Accordingly, safe and secure ATC system is mandatory as its failure or erroneous performance might cause serious consequences. In this paper, formal analysis of major components of ATC system and its safety properties is presented to prevent collision of aircrafts in the airspace. A step by step model is proposed to analyze the system and safety properties reducing complexity of the system using graph theory and Z notation. Initially, a network model of airspace for traffic flow management is presented. Then aircrafts with on-board system and ground-based controls are defined. For safety analysis, it is supposed that existence of two aircrafts in a smallest unit, block of airspace, is a collision. The issue of air crossing that is approaching two aircrafts to same point is also addressed. Based on these definitions abstract safety properties are defined by introducing a notion of protected area of an aircraft in front of it. Further, the safety properties are analyzed and extended by introduction of computer based air traffic controls. The formal specification is analyzed and validated using Z/Eves tool.

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Acknowledgments

The author is grateful to Dr. Tanzila SABA for checking similarity index of this paper through her turnitin account provided by Universiti Teknologi Malaysia (UTM).

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Authors and Affiliations

  1. Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Amjad Rehman

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  1. Amjad Rehman
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Correspondence to Amjad Rehman.

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Rehman, A. Machine learning based air traffic control strategy. Int. J. Mach. Learn. & Cyber. 12, 2151–2161 (2021). https://doi.org/10.1007/s13042-012-0096-6

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  • DOI: https://doi.org/10.1007/s13042-012-0096-6

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