Improved Binary Space Partition Method of Airspace Sectorization Considering Multiple Scenarios

Airspace sectorization is an air traffic management strategy which partitions the airspace into several sectors to accommodate traffic safe and efficient operation demands. The existing methods of airspace sectorization focus on the distribution of air traffic during up to one normal day without considering the distribution in other abnormal days induced by convective weather. In this paper, we propose a new airspace sectorization scheme considering multiple scenarios. We model the distribution of air traffic during a long time period using several scenarios with corresponding probability. We then propose a multi-objective optimization approach to optimize airspace sectorization for multiple scenarios that minimize the controller workload variance across different sectors, the expected controller overload and the flow across sector boundary. We also present an improved Binary Space Partition (iBSP) method to solve this kind of airspace sectorization problem. The iBSP method consists of discretizing search space and searching optimal cutting path. Crossing traffic trajectory only once approach was proposed to obtain simplified search space. A multi-objective optimization based depth first search algorithm was presented to find optimal cutting path. Experimental results show that the airspace sectorization solution had significant better performance in controller overload reduction but a slight loss of controller workload balance compared with that solution obtain by only considering a single normal scenario.