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The most troublesome factor in the work of an air traffic controller, which negatively affects the level of safety of air operations, is the uneven and variable distribution of air traffic intensity over time. The aim of the research is to develop a new AI based method of the air traffic controllers workload assessment, which will improve the safety and efficiency of flight operations at both large commercial airports and smaller non-commercial training and military airports. The innovative nature of the research results from the use of an artificial neural networks to model complex air traffic patterns, which takes into account different factors affecting the ATC workload. The optimal level of workload developed by the model will be verified by measuring the psychophysiological parameters of the ATC, which change under the influence of increased cognitive activity and stress, which results from the complexity of the movement situation.
