Investigation of strategies adopted by air traffic controllers in executing flight level changes for en-route air traffic

Abstract

New technological boundaries are defining the future state of Air Traffic Management (ATM). Advanced traffic modelling and artificial intelligence (AI) in ATM will increase sector capacity, reduce controller workload, and enhance capabilities to handle complex flows. These major changes reinforce the immanent need to understand how Air Traffic Controller Officers (ATCOs) manage traffic situations. This report aims to evaluate specific Air Traffic Control (ATC) strategies in handling common traffic flows to determine the best practice in the industry, which have not been done previously. The scope of this study is on strategies specific to handling 2 common traffic flow patterns, namely sequenced and convergent climbs, within the en-route airspace. Analysis was carried out for 6 case studies, each involving 11 ATCOs and 6 student participants. Specific strategies adopted by ATCOs were identified and evaluated based on defined metrics of operational performance, workload, fuel costs and ease of automation. Adoption rates for the strategies were also computed for ATCOs and student participants and compared against to validate and recommend appropriate strategies to the controller. Evidence of a heavy reliance on the human controller for the spatial management of aircraft and its complexity also points to the limitation of automation in ATC.