A chronological review of airspace sectorization approaches applied to enhancing air traffic management

Purpose This study aims to evaluate algorithms and optimization techniques used in airspace sectorization (AS) methods to handle air traffic demands for various classes during adverse weather and sudden traffic volume changes. Design/methodology/approach This study examines algorithms and optimization solutions for AS, including mathematical programming, constraint-based approaches, airspace complexity metrics, sector design principles, quality criteria and analyzes common objectives, methodologies and performance tradeoffs. Findings The region-based model (RBM) and graph-based model (GBM) aim for balanced workload distribution across airspace sectors. RBM accommodates constraints, although produces inconsistent boundaries. GBM represents airspace as a weighted graph, enabling dynamic sector configuration. However, do not directly display boundaries. The computational geometry-based model represents an alternative approach, though it is less effective for large airspaces and may violate geometric constraints. Practical implications This paper presents guidelines for researchers and academics engaged in efforts to enhance air traffic management, with a particular focus on the field of AS. The objective is to provide valuable insights into the field. The optimization technique can generate sector boundaries that correspond to those created by conventional methods, thereby facilitating balanced sectorization. Further research in this area will facilitate improvements to the sectorization process, resulting in the creation of more efficient sectors. Originality/value This study reviews dynamic airspace sectorization (DAS) and configuration (DAC) approaches to synthesizing prevalent AS models. It evaluates leading approaches’ performances, clarifying constraints, functions and challenges central to DAC and DAS. The paper highlights the outstanding airspace sectorization problem and proposes future research directions.