Complexity Metrics for Moving Sectors

28 June 2024

Complexity Metrics for Moving Sectors
^{S Göppel, M Schultz, E Itoh. IEEE Access (accepted)}

This study establishes a foundation for determining the complexity of moving sectors, addressing the current lack of standardized assessment tools. Moving sectors are a key element of efficient flow-centric air traffic flow management. Current air traffic flow management relies on sector-based approaches, with air traffic controller workload being a primary constraint on airspace capacity. The controller workload is influenced by air traffic complexity and airspace design. The introduction of moving sectors for prioritized traffic flows challenges existing complexity evaluation metrics. An in-depth analysis of available metrics revealed that only a few metrics could be reproduced and adapted to evaluate the complexity of moving sectors. Twenty-five impacting factors were identified and aggregated into five complexity metrics. Six moving sector configurations were derived, using air traffic scenarios in the Singapore flight information region. The five metrics were applied to calculate the complexity of the moving sectors. These metrics serve as a method indicating controller workload. Due to the absence of an operational concept for prioritized traffic flows, human-in-the-loop experiments could not be conducted to determine controller workload/taskload. However, fifteen air traffic controllers provided expert assessments of the air traffic scenarios, which were then compared with the calculated complexity values. Besides, the developed complexity metrics were employed to optimize the air traffic scenarios in the moving sectors by implementing complexity mitigation strategies, such as adjusted arrival times or shifting traffic flows. Findings reveal variability in complexity metrics and controller ratings, with a modest linear correlation. This suggests that the introduced five metrics capture certain aspects of complexity but also highlight the need for further exploration and integration into a unified complexity metric.