摘要
管制扇区是空域运行的基本单元,准确评估其容量对保障飞行安全,提高空域资源利用率,降低航班延误具有重要意义。为此研究了一种考虑WITI(Weather Impact Traffic Index,天气影响交通指数)与管制员工作负荷的扇区容量评估方法。将WITI值、扇区流量、天气影响区域和仿真容量作为输入,基于管制员工作负荷的容量作为输出,对灰色神经网络进行训练,形成多场景扇区容量评估网络。以杭州萧山机场进近扇区为例,应用神经网络对扇区容量进行了预测评估。实验结果证明,方法能够较准确地评估扇区容量,评估结果能够为管制员制定流量管理决策提供一定参考。
Control sector is the basic unit of airspace operation.Accurate assessment of its capacity is of great significance to ensure flight safety and efficiency,improve airspace resource utilization and reduce flight delays.To this end,a method of sector capacity assessment based on WITI(Weather Impact Traffic Index)and controllers′workload is studied.It selects WITI value,sector traffic flow,weather affected area and simulation capacity as the input,capacity based on controllers′workload as the output,to train the neural network and form a multi scenario sector capacity assessment network.At the end,the approach sector capacity of Hangzhou International Airport is assessed with the neural network.Results show that the method can assess the capacity of the sector comparatively accurately,and the assessment results can provide some reference for controllers to make flow management decisions.
作者
刘继新
朱学华
吴懿君
尹旻嘉
曾逍宇
LIU Ji-xin;ZHU Xue-hua;WU Yi-jun;YIN Min-jia;ZENG Xiao-yu(College of Civil Aviation,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)
出处
《航空计算技术》
2018年第6期11-14,19,共5页
Aeronautical Computing Technique
基金
国家自然科学基金项目资助(61304190
61773203)
