Air Traffic Controller Workload Estimation based on Speech Understanding Data with Visualization in Supervisor Interface

Abstract

ATC plays a crucial role in ensuring the safe and efficient management of airspace. This paper addresses the need for an improved Workload (WL) estimation calculus for Air Traffic Controllers (ATCo) with the aim of optimizing sector division and working schedules. The proposed approach utilizes a supervisor tool designed for the ATCo room supervisor, integrating data from a reliable Assistant-Based Speech Recognition system developed by the Controller Assistance Department of the German Aerospace Center (DLR). The motivation behind this paper stems from the current lack of a standardized measure for workload estimation in the Air Traffic Controller (ATCo) room. The existing methods either involve offline communication with the ATCo or online monitoring of individual parameters one at a time. This approach falls short in providing a comprehensive and reliable assessment of workload. Thus, the paper aims to address this limitation by developing a robust workload estimation system based on quantifiable parameters. By considering factors such as the number of callsigns, number of communications, actual duration time, and command complexity and type, the proposed system seeks to provide a more accurate and objective measure of workload. This motivation arises from the need to enhance efficiency and safety in air traffic control operations by enabling supervisors to make informed decisions based on reliable workload estimation. For this study, operational data from two approach sectors within a European airport's approach area hub were utilized. The choice to focus on these specific sectors allows for a targeted analysis of workload patterns and averages in a real-world air traffic control setting. By examining the workload dynamics in these sectors, the research aims to provide insights that can be applied to improve sector division and optimize working schedules in similar operational environments. The use of operational data adds relevance and practical applicability to the study, enabling the findings to be directly applicable to the management of air traffic control operations at the selected European airport.