基于L-M神经网络的空中交通复杂度评价方法研究

Evaluation Methods of Air Traffic Complexity Based on L-M Neural Network

随着空中交通流量的不断增长,航班延误日趋严重,迫切需要在保持安全水平的前提下提高空中交通管理能力。针对这一问题,本文在给定的空域条件下,从交通流、飞行活动特征和冲突三方面提出空中交通复杂度的评价指标,并考虑了管制员工作负荷的因素。以进入扇区的流量、改变高度的航空器比例、航空器速度改变次数、航空器航向改变次数、航空器通过扇区的平均时间和冲突数量作为空中交通复杂度的参数,利用L-M神经网络算法建立空中交通复杂度评价方法的数学模型。通过一个具体的数值算例,对比了LM算法与传统BP神经网络算法的计算结果,实验结果表明所提方法具有较高的逼近精度,验证了所提出方法的有效性和可行性。

Along with a steady increase in air traffic flow and increasingly severe flight delays,improvement in air traffic management capability is urgently needed on the precondition of maintaining the level of safety.To this end,an evaluation index of air traffic complexity,from three aspects of air traffic flow,flight characteristics and flight conflicts,is proposed in this paper for a certain airspace with controller workload taken into consideration.With flow entering a sector,percentage of aircraft altering altitude,times of speed adjustment,times of changing direction,average time for transiting the sector and number of conflicts taken as the parameters for airspace complexity,a mathematical model for evaluating air traffic complexity is put forward based on L-M neural network algorithm.Through a specific example,the L-M algorithm is compared with traditional algorithm in terms of their calculation results.The results show that the proposed technique possesses a high degree of precision,and therefore,is effective and feasible.