Dynamic Airspace Configuration

Dynamic Airspace Configuration: A Graph-Based Algorithm [1] 

In this paper, a new algorithm for dynamic airspace configuration (DAC) is developed based on graph theory. 

• A graph model is first constructed that accurately represents the air-route structure and air traffic in the National Airspace System (NAS). 

• The airspace configuration problem is then formulated as a graph-partitioning problem to balance the subgraph (sector) workload while satisfying the capacity constraint, which is efficiently solved by a spectral clustering method. Since the original spectral clustering method shows some undesirable properties, such as disconnected subgraphs and unbalanced partitions, an algorithm is proposed to refine the partitions. 

• Lastly, using the partitioned graph as an input, a novel airspace sectorization algorithm is developed, based on the graph search method. The new sectors computed by the sectorization algorithm have smooth boundaries and good geometrical shapes, which satisfy the minimum distance requirement (i.e., the sector boundaries are at least a minimum distance away from the airports, waypoints, and main air routes). 

The performance of the proposed dynamic airspace configuration algorithm is validated through various air-traffic scenarios with real air-traffic data.

Dynamic Sectorization Algorithm for Terminal Airspace [2]

Given its static and rigid structure, the current National Airspace System (NAS) lacks the ability to cope efficiently with the increasingly severe demand–capacity imbalances expected to develop over the coming years. To better accommodate the flexibility desired for future flight operations and to alleviate the demand–capacity imbalances, research initiatives have been conducted under the dynamic airspace configuration (DAC) concept. Although most past DAC researchers have focused on en route airspace, this paper investigates terminal airspace operations. 

A dynamic sectorization algorithm for terminal airspace is developed, which combines 

• a k-means clustering-based vertical sectorization algorithm, 

• an integer-programming-based horizontal sectorization algorithm, and 

• an α-shapes-based airspace sectorization algorithm. 

This dynamic sectorization algorithm is validated with real traffic data from several major international airports in the United States and simulated traffic data with projected future air routes and traffic patterns. Performance evaluation demonstrates that the algorithm can improve the efficiency of terminal airspace utilization and reduce traffic complexity.

Related Publications

1. J. Li, T. Wang, M. Savai, and I. Hwang, "A Graph-Based Algorithm for Dynamic Airspace Configuration," AIAA Journal of Guidance, Control and Dynamics, Vol. 33(4), p1082-1094, July-August 2010

2. J. Wei, V. Sciandra, I. Hwang, and W. Hall, “Design and Evaluation of a Dynamic Sectorization Algorithm for Terminal Airspace,” AIAA Journal of Guidance, Control and Dynamics, Vol.37(5), pp.1539-1555, September 2014