Development and Validation of an Airspace Complexity Indicator for Pre-Tactical Air Traffic Management

Abstract

The continuous growth of air traffic demand has increased congestion levels and controller workload, highlighting the need for reliable and operationally applicable tools to quantify airspace complexity. However, existing complexity metrics often rely on either a simplified representation of traffic or complex formulations that limit their interpretability and integration into operational environments. This paper proposes a novel airspace complexity indicator designed to provide a structured and operationally interpretable representation of sector-level complexity. The proposed indicator integrates multiple contributing factors, including traffic density, vertical distribution, temporal distribution, aircraft type variability, and traffic flow interactions, into a unified framework. The formulation is designed to balance representativeness and simplicity. The indicator is evaluated using real traffic data from the Madrid Area Control Center (ACC), through the analysis of multiple scenarios with different demand levels and temporal distributions. The evaluation assesses the ability of the indicator to capture known traffic patterns, identify complexity peaks, and distinguish between sectors with different operational characteristics. Results show that the proposed indicator consistently reflects variations in traffic demand and sector behavior, successfully identifying high-complexity regions and capturing both the spatial and temporal dynamics of air traffic. Furthermore, its integration within the Clusterized Aircraft Early Handover (CAEHO) framework demonstrates its capability to support pre-tactical decision-making by identifying capacity and demand imbalances and enabling more efficient traffic redistribution. Within the scope of the presented case studies, these results support the potential of the proposed indicator as a practical and interpretable tool for airspace complexity assessments; further validation against independent controller workload measurements and established complexity benchmarks is required to confirm its broader operational applicability.