Dynamic airspace sectorization via improved genetic algorithm

This paper deals with dynamic airspace sectorization （DAS） problem by an improved genetic algorithm （iGA）. A graph model is first constructed that represents the airspace static structure. Then the DAS problem is formulated as a graph-partitioning problem to balance the sector workload under the premise of ensuring safety. In the iGA, multiple populations and hybrid coding are applied to determine the optimal sector number and airspace sectorization. The sector constraints are well satisfied by the improved genetic operators and protect zones. This method is validated by being applied to the airspace of North China in terms of three indexes, which are sector balancing index, coordination workload index and sector average flight time index. The improvement is obvious, as the sector balancing index is reduced by 16.5 %, the coordination workload index is reduced by 11.2 %, and the sector average flight time index is increased by 11.4 % during the peak-hour traffic.

Dynamic reconfiguration for en-route airspace

As the air traffic demand is anticipated to be increased significantly in the near future,dynamic and effective allocation of the airspace resource is becoming a world-wide focus in the research field of air traffic management（ATM）.Taking the U.S.targeting the en-route airsapce,a dynamic airspace configuration（DAC） algorithm to reconfigure the airspace in consideration of higher efficiency and safety is presented.First,a modeling technique based on graph theory is proposed to generate a mathematical model for the airspace,and then,the graph model is partitioned into subgraphs for the purpose of sectorizatoin.The final step generates sector configuration with desirable geometry shape.Through analysis on the Cleveland airspace center（ZOB） in the U.S.,the algorithm is proved to be robust to time-varying traffic load.

Sector Capacity Estimation Based on Differentiated Routes and Busy Levels

Sector capacity estimation plays an important role in applied research of airspace management.Previous researches manifest that sector capacity should be influenced by its standard flow,or routes in that sector.However,if air traffic controller(ATCO)workload busy levels(level of proactivity of an ATCO)are ignored,the estimated sector capacity may not be accurate.There is a need to compare the estimated sector capacity with and without busy levels consideration,both with differentiated routes consideration.This paper proposes a method for sector capacity estimation based on ATCO workload considering differentiated routes and busy levels.Firstly,the main routes in the sector are identified,and for each route,the ATCO workload per flight is determined.Secondly,the workload for each route at three busy levels is determined.Regression analysis is then applied to determine the relationship between workload and the number of flights(with and without considering busy levels)in 15 min and 1h time slices.Sector capacity is then determined on the basis of a specified workload threshold,for the two cases with and without considering busy levels.Comparing the two scenarios and following validation by ATCO survey,it is found that capacity estimation considering busy levels is a more realistic and accurate approach.The validated capacity values for the Zhengzhou approach(ZHCC AP)airspace sector accounting for the busy levels were determined accurately as 10 and 33 flights for the 15 min and 1h slices,respectively.The corresponding results without considering busy levels were 12 and 41 flights for the 15 min and 1h time slices,respectively.

Dynamic airspace configuration method based on a weighted graph model

This paper proposes a new method for dynamic airspace configuration based on a weighted graph model. The method begins with the construction of an undirected graph for the given airspace, where the vertices represent those key points such as airports, waypoints, and the edges represent those air routes. Those vertices are used as the sites of Voronoi diagram, which divides the airspace into units called as cells. Then, aircraft counts of both each cell and of each air-route are computed. Thus, by assigning both the vertices and the edges with those aircraft counts, a weighted graph model comes into being. Accordingly the airspace configuration problem is described as a weighted graph partitioning problem. Then, the problem is solved by a graph partitioning algorithm, which is a mixture of general weighted graph cuts algorithm, an optimal dynamic load balancing algorithm and a heuristic algorithm. After the cuts algorithm partitions the model into sub-graphs, the load balancing algorithm together with the heuristic algorithm transfers aircraft counts to balance workload among sub-graphs. Lastly, airspace configuration is completed by determining the sector boundaries. The simulation result shows that the designed sectors satisfy not only workload balancing condition, but also the constraints such as convexity, connectivity, as well as minimum distance constraint.