Air Traffic Control Complexity and Safety

Abstract

“Air traffic control (ATC) complexity” can be defined as the interaction between air traffic and the sector characteristics through which this air traffic flies. Research indicates that complexity is the prime determinant of the air traffic controller's workload. Research has attempted to define and classify complexity variables that have been used to assess the capacity of en route airspace in Europe and North America. Such variables can also be used to assist in ATC sector design by highlighting those complexity factors that can lead to high workload and potentially risky scenarios for the controller. With the use of a structured interview technique, a study analyzed these ATC complexity variables for their impact on controller workload and safety. Seventy-nine air traffic controllers were interviewed about the factors that affected their workload. These controllers were based in 14 area control centers in Europe, including those of high complexity, such as Maastricht, Netherlands, as well as in nine centers in Asia, one in Africa, and Mumbai, India, a region with major traffic growth in recent years. On the basis of these interviews, a taxonomy of more than 50 complexity variables subdivided into 11 major groupings, such as traffic mix and entry and exit points, has been developed. In addition to rating of the impact of the individual complexity factors to assess their effect on airspace, interactions between complexity variables were deduced from the interviews. These interactions enable assessment of the factors that add to layers of complexity for controllers. Such a taxonomy can assist airspace planners in designing sectors that avoid complex ATC situations. How the impact of such complexity factors can be investigated in sector design is outlined.

Air Traffic Controllers' Situation Awareness and Workload under Dynamic Air Traffic Situations

“…To evaluate the effects of various air-traffi c situations on the controller's SA and workload, the mean values on each component comprising those two constructs were compared. For this analysis purpose, we used ANOVA, which provides a good method to verify statistical difference between two groups or more with mean values (Majumdar and Ochieng 2007) . As SART components contain workload contents, general relationships of these two constructs were analyzed using correlation and regression analysis.…”

“…The controllers are responsible for managing a complex mixture of air traffi c from commercial, general, corporate, and military aviation. Thus, they play a vital role in the aviation system by controlling the air traffi c under their jurisdiction in a safe and expedient manner ( Majumdar and Ochieng 2007 ).…”

Relationship between Air Traffic Demand, Safety and Complexity in High-Density Airspace in Europe

“…Other approaches to define, measure, manage or even reduce air traffic or airspace complexity have recently appeared, opening a new field for further application of the complexity metric [29][30][31][32]. The above-mentioned overview also shows that great attention was given to modelling and measuring complexity in the en route environment related to the ATCo workload.…”

Learning Air Traffic as Images: A Deep Convolutional Neural Network for Airspace Operation Complexity Evaluation

“…To sum up, SOC domains the air traffic control workload, which is essential in the air traffic operation, leading to its extraordinary role in air traffic management, e.g., airspace reconfiguration, air traffic flow management, and allocation of ATCo resources. erefore, accurately evaluating the SOC is a hot topic both in research and practical applications [9][10][11].…”