CAJ | 학술논문

为了全面分析扇区复杂性,将其分解为结构复杂性和运行复杂性.借鉴已有的研究成果,围绕结构特征和运行特征,分别建立了多维指标体系.利用主成分分析方法提炼指标信息,评估扇区的结构复杂性和运行复杂性.最后采用k-means聚类算法对多个扇区进行聚类分析,选取Dunn指标评价聚类质量,实现了对扇区复杂程度的最佳等级划分,同时对复杂性指标分析结果进行了验证.实例表明,复杂性计算结果能够较好地体现多个指标的综合影响,区分不同扇区的复杂程度,聚类结果与实际情况相符.该结论可以为空域规划和管理提供参考意见.
위료전면분석선구복잡성,장기분해위결구복잡성화운행복잡성.차감이유적연구성과,위요결구특정화운행특정,분별건립료다유지표체계.이용주성분분석방법제련지표신식,평고선구적결구복잡성화운행복잡성.최후채용k-means취류산법대다개선구진행취류분석,선취Dunn지표평개취류질량,실현료대선구복잡정도적최가등급화분,동시대복잡성지표분석결과진행료험증.실례표명,복잡성계산결과능구교호지체현다개지표적종합영향,구분불동선구적복잡정도,취류결과여실제정황상부.해결론가이위공역규화화관리제공삼고의견.
In order to analyze sector complexity comprehensively, this paper decomposes it into structure complexity and operation complexity. Multi-dimensional metrics focusing on airspace structure characteristics and traffic operation characteristics are constructed respectively according to present research. Primary component analysis is used to refine metrics information, from which structure complexity and operation complexity are evaluated. Multiple sectors are divided into different clusters by k-means clustering algorithm. The Dunn indicator is used to evaluate clustering results, which can help us decide the optimal clustering number. The clustering results are treated as the best classification for sector complexity, which could verify the metrics evaluation results. In the case of sector samples, sector structure complexity and operation complexity can well reflect the comprehensive effect of multiple metrics, distinguish complex degree of different sectors. The clustering results are consistent with the actual situations. These conclusions could provide recommendations for airspace planning and management.