基于图论和遗传算法的终端扇区划分方法

为了均衡终端区扇区的工作负荷,保障扇区边界的合理性,研究了终端扇区的划分方法.针对建立的扇区划分数学模型,提出了3阶段的划分方法.首先,采用voronoi图和遗传算法完成初始划分,实现均衡负荷的目标;其次,提出了基于voronoi图和基于maklink图的2种扇区二次划分方法,满足最小飞行时间及最小距离约束;最后,应用分段线性拟合的方法消除扇区边界的锯齿状,以及满足扇区凸形约束.以广州终端为例进行了仿真计算.应用提出的方法,给出了2种划分结果,都能够使得各扇区的工作负荷在2880s以内,差值不超过350s,扇区边界较为平滑,且计算时间明显减少.仿真计算结果说明方法是可行和有效的,也为使用者提供了灵活的选择.     

Fluctuations in airport arrival and departure traffic: A network analysis

Air traffic is a typical complex system,in which movements of traffic components(pilots,controllers,equipment,and environment),especially airport arrival and departure traffic,form complicated spatial and temporal dynamics.The fluctuations of airport arrival and departure traffic are studied from the point of view of networks as the special correlation between different airports.Our collected flow volume data on the time-dependent activity of US airport arrival and departure traffic indicate that the coupling between the average flux and the fluctuation of an individual airport obeys a certain scaling law with a wide variety of scaling exponents between 1/2 and 1.These scaling phenomena can explain the interaction between the airport internal dynamics(e.g.queuing at airports,a ground delay program and following flying traffic) and a change in the external(network-wide) traffic demand(e.g.an increase in traffic during peak hours every day),allowing us to further understand the mechanisms governing the collective behaviour of the transportation system.We separate internal dynamics from external fluctuations using a scaling law which is helpful for us to systematically determine the origin of fluctuations in airport arrival and departure traffic,uncovering the collective dynamics.Hot spot features are observed in airport traffic data as the dynamical inhomogeneity in the fluxes of individual airports.The intrinsic characteristics of airport arrival and departure traffic under severe weather is discussed as well.