Large Scale Multi-Objective Optimization for Dynamic Airspace Sectorization

Abstract

A key limitation in accommodating continuing air traffic growth is the fixed airspace structure (sector boundaries), which is largely determined by historical flight profiles that have evolved over time. The sector geometry has stayed relatively constant despite the fact that route structures and demand have changed dramatically over the past decade.

Dynamic Airspace Sectorization (DAS) is a concept where the airspace is redesigned dynamically to accommodate changing traffic demands. Various methods have been proposed to dynamically partition the airspace to accommodate traffic growth while also considering other sector constraints and efficiency metrics. However, these approaches suffer several operational drawbacks, and their computational complexity increases exponentially as the airspace size and traffic volume increase.

In this thesis, I experimentally evaluate and identify gaps in existing 3D sectorization methods, and propose an improved Agent Based Model (iABM) to address these gaps. I also propose three additional models using KD-Tree, Support Plane Bisection (SPBM) and Constrained Voronoi Diagrams (CVDM) in 3D, to partition the airspace to satisfy the convexity constraint and overcome high computational cost inherent in agent-based approaches. I then look into optimizing the airspace sectors generated by these four models (iABM, KD-Tree, SPBM, and CVDM), using a multi-objective optimisation approach with Air Traffic Controller (ATC) task load balancing, average sector flight time, and minimum distance between sector boundaries and traffic flow crossing points as the three objectives. The performance and efficiency of the proposed models are demonstrated by using sample air traffic data. Experimental results show that all the approaches have strengths and weaknesses. iABM has the best performance on task load balancing, but it can't satisfy the convexity constraint. SPBM and CVDM perform worse than iABM on task load balancing but better on average sector flight time, and they can satisfy the convexity constraint. The KD-tree based model is the most efficient, but not effective as it performed poorly on the given objectives because of its representational bias, which also limits its use in an operational environment.

To further investigate SPBM and CVDM for national airspace sectorization, a real time air traffic monitoring and advisory system, called TOP-LAT (Trajectory Optimization and Prediction of Live Air Traffic), is developed and implemented. TOP-LAT is a real time system, synthesizing real time air traffic data to measure and analyse airspace capacity, airspace safety, air traffic flow and aviation emission, to enable ATM participants to access timely, accurate and reliable information for ATM decisions. TOP-LAT provides an ATM environment to evaluate and investigate the advanced ATM concepts, such as DAS. A number of experiments of Australian airspace sectorization by the two proposed DAS models are conducted in this thesis. In these experiments, the current and projected air traffic demands are generated based on public statistics, and some future ATM concepts (e.g. User Preferred Trajectory) are prototyped in order to investigate the performances of the proposed models. The results show that both models have advantages over the current airspace sector configurations in terms of task load balancing, longer flight sector time, larger minimum distance between sector boundaries and traffic flow crossing points, and reduced maximum task load for ATC. These experiments also show that Both models have the capability to be compatible with other advanced ATM concepts. However, no single approach can meet all complex air traffic management objectives. It is the air traffic flow pertaining to the kind of airspace and the associated traffic complexity which can determine the best approach for dynamic sectorization.