摘要
扇区复杂度作为管制员工作负荷和动态空域配置的重要参考依据,需要事先准确地对其进行评估。本文针对有监督复杂度数据集存在的小样本问题,提出基于条件生成对抗网络的扇区复杂度评估框架。首先,构建交通流量、航空器性能和潜在冲突这3类复杂度指标,并结合主观复杂度等级得到标定样本;其次,利用条件生成对抗网络设计有标记样本生成算法,获得增广数据集;最后,分别采用逻辑回归、支持向量机和随机森林算法建立复杂度评估模型。以中南区域扇区为例,从定性和定量的视角验证生成样本的有效性,并在多种训练集配置下对比各模型评估结果。研究结果表明:条件生成对抗网络在200次迭代后逐步收敛至稳定;生成样本与真实样本的绝大多数指标在均值上的相对误差小于5%,在标准差上的相对误差大于5%;在多分类评价指标下,增广数据集对3种模型整体评估精度分别提升11.77%、11.04%和8.34%。本文提出的评估框架可以在有限数据条件下提高样本多样性,是解决扇区复杂度评估问题的一种有效方法。
Sector complexity is an important reference for controller workload and dynamic airspace configuration,which needs to be accurately evaluated in advance.To handle the difficulty of the small sample size in supervised complexity data sets,a sector complexity evaluation framework based on Conditional Generative Adversarial Networks(CGAN)was proposed.Firstly,three types of complexity factors,i.e.,traffic flow,aircraft performance,and potential conflicts,were constructed,and subjective complexity levels were combined to obtain calibration samples.Then,the CGAN was used to design a labeled sample generation algorithm to get an augmented data set.Finally,Logistic Regression(LR),Support Vector Machines(SVM),and Random Forest(RF)algorithms were used to build complexity evaluation models.Taking the sector in the central-south region as an example,the effectiveness of the generated samples was verified from qualitative and quantitative perspectives.The evaluation results of each model were compared under multiple training set configurations.The results show that CGAN gradually converges to stability after 200 iterations.The relative error between the generated sample and the real sample for most factors in the mean is less than 5%,and the relative error in the standard deviation is more than 5%.Under the multi-class evaluation indicators,the augmented data set improves the overall evaluation accuracy of the three models by 11.77%,11.04%,and 8.34%,respectively.The proposed evaluation framework can improve sample diversity under limited data conditions,and it is an effective method for the sector complexity evaluation.
作者
张魏宁
胡明华
杜婧涵
尹嘉男
ZHANGWei-ning;HU Ming-hua;DU Jing-han;YIN Jia-nan(School of Civil Aviation,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)
出处
《交通运输系统工程与信息》
EI
CSCD
北大核心
2021年第6期226-233,288,共9页
Journal of Transportation Systems Engineering and Information Technology
基金
国家自然科学基金(52002178,71731001)
江苏省自然科学基金(BK20190416)。
关键词
航空运输
扇区复杂度评估
条件生成对抗网络
复杂度指标
增广数据集
样本多样性
air transportation
sector complexity evaluation
Conditional Generative Adversarial Networks
complexity index
augmented data set
samples diversity
作者简介
张魏宁(1992-),男,河北石家庄人,博士生。;通信作者:尹嘉男,j.yin@nuaa.edu.cn。
