Study of the Impact of Traffic Flows on the ATC Actions

Abstract

It has always been a topic of great interest in air transport management to be able to estimate controller workload. So far, research has not had the opportunity to make use of real data on the controller’s actions. We have enough data to be able to use machine learning methods. The aim of this work is to predict the controller’s actions to know his workload. Several machine learning models were tested to try different combinations of features and the selected algorithms and two models were finally chosen. The predictions provided by the models are good enough to be used when a first approximation of the workload in a sector is to be obtained. Finally, explainability techniques were employed to discover the patterns found by the AI in the machine learning models. Thanks to these techniques, we can build a profile of the critical flights that increase the workload the most.

Mitigating the Negative Effect of Air Traffic Controller Mental Workload on Job Performance: The Role of Mindfulness and Social Work Support

“…The Civil Air Navigation Services Organization (CANSO) indicated variations in the performance of air traffic services (ATS) in their annual performance reports, which are due to various factors such as on-job training, air traffic management systems, availability of air traffic control units, operational procedures, and management styles. Despite the numerous types of air transport tools that help ATCOs' mental information processing [6,7], the role of an ATCO is still ranked as an occupation with the highest mental workload within the aviation industry [8]. Mental workload is inevitable in ATCOs' jobs; however, its impact on job performance needs to be managed such that it does not compromise flight safety, knowing that various air navigation services providers have variations in their operational processes and capabilities.…”