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Related Experiment Video

Updated: Jul 3, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

When and how to partition airspace: a data-driven control framework for dynamic airspace sectorization based on

Jinghan Du¹, Hongwei Li¹, Weining Zhang²

¹College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, 618307, China.

Scientific Reports

|July 1, 2026

Summary

This study introduces a data-driven framework for dynamic airspace sectorization (DAS) to manage changing air traffic. The new approach optimizes sector shapes and timing, improving airspace management flexibility.

Keywords:

Data-driven Dynamic airspace sectorization Multi-objective optimization Receding horizon Sector similarity

Related Experiment Videos

Last Updated: Jul 3, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Area of Science:

Air Traffic Management
Operations Research
Computer Science

Background:

Dynamic airspace sectorization (DAS) is crucial for adapting to evolving air traffic and weather conditions.
Existing methods face challenges in determining optimal sectorization timing and configuration.
Current airspace management requires enhanced flexibility and efficiency.

Purpose of the Study:

To develop a data-driven control framework for dynamic airspace sectorization.
To address the 'when-to-do' and 'how-to-do' aspects of airspace re-sectorization.
To improve the adaptability and efficiency of air traffic management systems.

Main Methods:

Developed a knowledge construction module with sector generation, controller workload, and sector similarity models.
Constructed a multi-objective receding horizon optimization (RHO) model for sectorization scheme generation.
Implemented a decision execution module using multi-criteria decision analysis for re-sectorization timing and effectiveness analysis.
Utilized Singapore Flight Information Regions (FIRs) trajectory data for empirical assessment.

Main Results:

The proposed framework generates serialized optimal airspace sectorization schemes with high similarity.
Compared to single interval optimization (SIO), the RHO model offers improved sector shape adaptation.
The decision execution module effectively determines optimal re-sectorization timing based on future traffic.
Empirical analysis validated the framework's effectiveness using real-world flight data.

Conclusions:

The data-driven framework enhances dynamic airspace sectorization by optimizing both scheme generation and timing.
The approach provides a more flexible and efficient solution for modern air traffic management.
This methodology offers a significant improvement over traditional single interval optimization methods.