Abstract
PurposeThe purpose of this paper is to set out a methodology for characterising the complexity of air traffic control (ATC) sectors based on individual operations. This machine learning methodology also learns from the data on which the model is based.Design/methodology/approachThe methodology comprises three steps. Firstly, a statistical analysis of individual operations is carried out using elementary or initial variables, and these are combined using machine learning. Secondly, based on the initial statistical analysis and using machine learning techniques, the impact of air traffic flows on an ATC sector are determined. The last step is to calculate the complexity of the ATC sector based on the impact of its air traffic flows.FindingsThe results obtained are logical from an operational point of view and are easy to interpret. The classification of ATC sectors based on complexity is quite accurate.Research limitations/implicationsThe methodology is in its preliminary phase and has been tested with very little data. Further refinement is required.Originality/valueThe methodology can be of significant value to ATC in that when applied to real cases, ATC will be able to anticipate the complexity of the airspace and optimise its resources accordingly.
Highlights
Introduction and objectivesThe objective of the air traffic management (ATM) system is to enable efficient, safe operations for airspace users (Gallego et al, 2018a)
The data correspond to operations in five air traffic control (ATC) sectors of Spanish airspace during 2019
The results produced by the model are logical from an operational point of view. They enable ATC to anticipate the real complexity of the airspace and optimise its resources
Summary
The objective of the air traffic management (ATM) system is to enable efficient, safe operations for airspace users (Gallego et al, 2018a). The number of flights is forecast to almost double by 2035 (EUROCONTROL, 2010). Within this context, Europe has a very complex transport network, air transport, due to the high level of mobility of its inhabitants (Samolej et al, 2021). The complexity of the operational scenario depends in part on the variability between the predicted and actual trajectories of the aircraft. The actual trajectory differs from the predicted one due to several factors, including the weather forecast, the integration of operational information and operational uncertainty (Gallego et al, 2018b)
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