The Relationship of Sector Characteristics to Operational Errors

Rodgers, Mark D.; Mogford, Richard H.; Mogford, Leslye S. · 1998 · ROSA P / United States. Department of Transportation. Federal Aviation Administration. Office of Aviation. Civil Aerospace Medical Institute

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Summary

This study investigates the relationship between air traffic control (ATC) sector characteristics and the occurrence of operational errors (OEs) in the National Airspace System. Motivated by a lack of research directly linking ATC complexity factors to error causality, the authors aimed to identify how airspace design and traffic flow patterns influence controller workload and situational awareness. The research was exploratory, seeking to fill a void in understanding how the controller’s work domain—specifically airspace geometry and traffic dynamics—contributes to safety incidents, rather than focusing solely on human failure or equipment issues. The methodology combined a comprehensive literature review with a detailed empirical analysis of OE data from the Atlanta Air Route Traffic Control Center (ARTCC) for the years 1992 through 1995. Data were collected using the Systematic Air Traffic Operations Research Initiative (SATORI) system, which allows for the reconstruction of error scenarios, alongside facility reviews and complexity questionnaires. The 45 sectors in the Atlanta ARTCC were categorized into zero-, low-, and high-error groups. Statistical analyses, including factor analysis, correlations, and ANOVA, were performed to examine fifteen sector and traffic flow variables, such as sector size, weather, radio frequency congestion, and traffic density, in relation to OE frequency and severity. The results demonstrated a statistically significant relationship between sector complexity and OE rates. Factor analysis identified three primary dimensions characterizing sectors: Traffic Activity, Size, and Military presence. Four variables—Weather, Radio Frequency Congestion, Total Complexity, and Average Complexity—were significantly higher in the high-error group compared to the zero-error group. Conversely, sector size was smaller in high-error sectors. While a multiple correlation existed between overall OE rate and a subset of complexity measures, predictive accuracy was low, limiting practical application. Regarding situational awareness (SA), the only statistically significant difference between errors with and without SA was horizontal separation; greater horizontal separation was associated with errors where controllers maintained awareness. High-error sectors were characterized by low SA, and errors lacking SA were more frequent in sectors with military operational restrictions. The study concludes that increased sector complexity is associated with reduced controller situational awareness, leading to a higher frequency and severity of operational errors. Although the predictive models lacked sufficient power for immediate practical use, the findings establish a firm theoretical link between sector characteristics and error occurrence. These results imply that sector design and traffic management strategies should account for complexity factors such as size and congestion to mitigate workload and improve safety. The authors recommend further data collection to validate these findings and support the development of decision-support systems and improved sector designs.

Key finding

High-error sectors were characterized by smaller sector size and higher complexity measures, and were associated with lower controller situational awareness during errors.

Methodology

dataset

Sample size: 45

Provenance

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Topics

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• pre crash contributing factors