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.

Air route network optimization in fragmented airspace based on cellular automata

Air route network optimization,one of the essential parts of the airspace planning,is an effective way to optimize airspace resources,increase airspace capacity,and alleviate air traffic congestion.However,little has been done on the optimization of air route network in the fragmented airspace caused by prohibited,restricted,and dangerous areas（PRDs）.In this paper,an air route network optimization model is developed with the total operational cost as the objective function while airspace restriction,air route network capacity,and non-straight-line factors（NSLF） are taken as major constraints.A square grid cellular space,Moore neighbors,a fixed boundary,together with a set of rules for solving the route network optimization model are designed based on cellular automata.The empirical traffic of airports with the largest traffic volume in each of the 9 flight information regions in China's Mainland is collected as the origin-destination（OD） airport pair demands.Based on traffic patterns,the model generates 35 air routes which successfully avoids 144 PRDs.Compared with the current air route network structure,the number of nodes decreases by 41.67%,while the total length of flight segments and air routes drop by 32.03% and 5.82% respectively.The NSLF decreases by 5.82% with changes in the total length of the air route network.More importantly,the total operational cost of the whole network decreases by 6.22%.The computational results show the potential benefits of the model and the advantage of the algorithm.Optimization of air route network can significantly reduce operational cost while ensuring operation safety.