A Method Framework for Automatic Airspace Reconfiguration-Monte Carlo Method for Eliminating Irregular Sector Shapes...

Area of Science:

• Air Traffic Management
• Operations Research
• Computational Geometry

Background:

• Increasing air traffic demand necessitates airspace sectorization for improved throughput and controller support.
• Existing methods, like the simulated annealing algorithm (SAA), can produce irregular sector shapes, complicating air traffic control.

Purpose of the Study:

• To develop an automated and reasonable method framework for airspace sectorization (AS).
• To create an advisory tool for air traffic controllers, specifically addressing irregular sector shapes.
• To enhance the reliability and efficiency of airspace design processes.

Main Methods:

• A region growth method combined with simulated annealing algorithm (SAA) for initial sectorization.
• Development of two graph cutting methods for post-processing: dynamic Monte Carlo by changing flexible vertex locations (MC-CLFV) and Monte Carlo by radius changing (MC-RC).
• Implementation of a methodology framework and software for assistant design and analysis.

Main Results:

• The proposed framework automatically generates reasonable airspace sector designs meeting specified criteria.
• The developed methods effectively eliminate irregular sector shapes generated by SAA.
• Experimental results validate the framework's capability for automated and reasonable sector scheme generation.

Conclusions:

• The developed airspace sectorization framework provides an automated and reliable solution for airspace design.
• The methodology serves as an assistant tool for air traffic planners, improving efficiency and reducing workload.
• This work lays the groundwork for more intelligent airspace reconstruction methods.