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作 者:董襄宁[1] 胡明华[1] 李孝荣 Dong Xiangning;Hu Minghua;Li Xiaorong(National Key Laboratory of Air Traffic Flow Management,Nanjing University of Aeronautics and Astronautics,Nanjing,211106,China;China International Engineering Consulting Corporation,Beijing,100048,China)
机构地区:[1]南京航空航天大学国家空管飞行流量管理技术重点实验室,南京211106 [2]中国国际工程咨询有限公司,北京100048
出 处:《数据采集与处理》2019年第5期908-914,共7页Journal of Data Acquisition and Processing
基 金:国家自然科学基金(61573181)资助项目,国家自然科学基金民航联合基金(U1333202)重点资助项目
摘 要:运用协方差矩阵和系统聚类法对区域管制扇区复杂性指标间关系进行研究。基于对区域管制扇区结构和流量分布特征的分析,构建7个复杂性指标。以国内37个区域管制扇区数据为样本,结合可视化协方差矩阵阐明复杂性指标相关性。研究表明,区域各扇区面积相差悬殊(变异系数194.75%),但流量在不同路径的分布均匀程度比较一致(变异系数9.02%);扇区范围越大,往往流量在各路径上的分布越均匀(相关系数0.54~0.59),但扇区流量反而越低(相关系数-0.06^-0.13);以并类距离0.70为界,扇区复杂性指标分为静态和动态两类,且静态指标共性强于动态指标。This paper addresses the relationship among the complex indicators of area control sectors using covariance matrix and hierarchical clustering method.Seven complexity indicators are built considering the structural and flow distribution features of area control sectors.Thirty-seven area control sectors in China are chosen to clarify the correlations among complexity indicators using visualized covariance matrix.It is found that the area of each sector varies greatly(coefficient of variation is 194.75%),while flow distribution of different routes in sample sectors has little change(coefficient of variation is 9.02%).The larger the sector range is,the more uniform the flow distribution is on each path(correlation coefficient is 0.54—0.59);the larger the sector range is,the lower the sector flow is(correlation coefficient is-0.06—-0.13).Single linkage method is introduced to cluster these complexity indicators.Clustering tree shows that static complexity indicators and dynamic complexity indicators can be well distinguished with the distance between classes of 0.70.It is found that static complexity indicators share more commonness than dynamic ones.
关 键 词:航空运输 扇区特征 系统聚类法 扇区复杂性指标 聚类树 协方差矩阵
分 类 号:V355[航空宇航科学与技术—人机与环境工程] U8[交通运输工程]
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