摘要
This paper introduces an innovative approach to the synchronized demand-capacity balance with special focus on sector capacity uncertainty within a centrally controlled collaborative air traffic flow management(ATFM)framework.Further with previous study,the uncertainty in capacity is considered as a non-negligible issue regarding multiple reasons,like the impact of weather,the strike of air traffic controllers(ATCOs),the military use of airspace and the spatiotemporal distribution of nonscheduled flights,etc.These recessive factors affect the outcome of traffic flow optimization.In this research,the focus is placed on the impact of sector capacity uncertainty on demand and capacity balancing(DCB)optimization and ATFM,and multiple options,such as delay assignment and rerouting,are intended for regulating the traffic flow.A scenario optimization method for sector capacity in the presence of uncertainties is used to find the approximately optimal solution.The results show that the proposed approach can achieve better demand and capacity balancing and determine perfect integer solutions to ATFM problems,solving large-scale instances(24 h on seven capacity scenarios,with 6255 flights and 8949 trajectories)in 5-15 min.To the best of our knowledge,our experiment is the first to tackle large-scale instances of stochastic ATFM problems within the collaborative ATFM framework.
引入了一种创新方法,实现了协同空中交通流量管理框架下扇区容量不确定性的需求与容量平衡。受极端天气、空军活动、管制员工作负荷等不可忽视的隐性因素影响,空域容量具有不确定性,进而影响流量管理优化结果。本文重点研究了扇区容量不确定性对需求与容量平衡优化,以及对空中交通流量管理优化的影响。在协同流量管理框架下实施多种策略,如延误指派和改航绕飞等管理交通流。进而提出了一种场景优化方法解决扇区容量的不确定性。结果显示,所提方法可以实现更好的需求与容量的平衡,并在空中交通流量管理问题中得到近似最优解,解决大规模的流量优化实例(24 h下的7个容量场景,6255个航班以及8949条航迹)只需要5~15 min。本文实验计算是已知的首次在协同流量管理框架下解决大规模随机性空中交通流量管理问题的有效实例。
