基于指标体系的扇区复杂性评估方法

交通运输系统工程与信息 ›› 2015, Vol. 15 ›› Issue (5): 136-141.

基于指标体系的扇区复杂性评估方法

丛玮¹，胡明华^*1，谢华¹，张晨²

1. 南京航空航天大学民航学院，南京211106；2. 民航华东地区空中交通管理局，上海200000

收稿日期:2015-04-02 修回日期:2015-05-20 出版日期:2015-10-25 发布日期:2015-10-28
作者简介:丛玮（1988-）男，江苏南通人，博士生.
基金资助:
国家自然科学基金（61304190, 71301074）；江苏省自然科学基金面上项目（BK20131366）

An Evaluation Method of Sector Complexity Based on Metrics System

CONGWei¹，HU Ming-hua¹，XIE Hua¹，ZHANG Chen2

1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; 2. East China Regional Air Traffic Management Bureau of Civil Aviation Administration, Shanghai 200000, China

Received:2015-04-02 Revised:2015-05-20 Online:2015-10-25 Published:2015-10-28

摘要/Abstract

摘要：

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.

关键词: 航空运输, 扇区复杂性, 指标体系, 主成分分析, k-means聚类

Abstract:

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.

Key words: air transportation, sector complexity, metrics system, primary component analysis, k-means clustering

中图分类号:

丛玮，胡明华，谢华，张晨. 基于指标体系的扇区复杂性评估方法[J]. 交通运输系统工程与信息, 2015, 15(5): 136-141.

CONGWei，HU Ming-hua，XIE Hua，ZHANG Chen. An Evaluation Method of Sector Complexity Based on Metrics System[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(5): 136-141.

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链接本文: http://www.tseit.org.cn/CN/abstract/abstract19074.shtml

http://www.tseit.org.cn/CN/Y2015/V15/I5/136

参考文献

[1] Maria P, Luigi P, Stephane P, et al. Toward air traffic complexity assessment in new generation air traffic management systems[J] . IEEE Transactions on Intelligent Transportation Systems, 2011, 12(3): 809- 818.

[2] Mogford R H, Guttman J A, Morrow S L, et al.The complexity construct in air traffic control: a review and synthesis of the literature[R].DOT/FAA/CT- TN95/22, 1995.

[3] Laudeman I V, Shelden S G, Branstrom R, et al. Dynamic density: An air traffic management metric[R] . NASA/ TM -1998-112226, 1998.

[4] Chittayong S, Peng W, Steven L, et al. Evaluation of stream air traffic operations by adapting dynamic density complexity measure[C]// Proceedings of 12th AIAA Aviation Technology, Integration, and Operations Conference, Indianapolis, 2012.

[5] Delahaye D, Puechmorel S. Air traffic complexity: towards intrinsic metrics[C]// 3rd USA/Europe ATM R&D Seminar, Napoli, Italy, 2000.

[6] Delahaye D, Puechmorel S, Hansman R J, et al. Air traffic complexity map based on non linear dynamical systems[J]. Air Traffic Control Quarterly, 2004, 12(4): 367-388.

[7] Chen Z, Jin Z, Ming H H. Air traffic complexity based on alliance effects[C]//28th Digital Avionics Systems Conference. Florida: Orlando, 2009:7.A.1-1-7.A.1-9.

[8] Jin Z, Chen Z, Ming H H. Airspace behavior modeling considering flight intent[C]//28th Digital Avionics Systems Conference. Florida: Orlando, 2009:7.A.2-1-7. A.2-10.

[9] Lee K, Feron E, Pritchett A. Air traffic complexity[C]// 44th Annual Allerton Conference. 2006:1358-1363.

[10] Lee K, Feron E, Pritchett A. Air traffic complexity: an input-output approach[C]//7th USA/Europe ATM R&D Seminar, 2007.

[11] Lee K, Feron E, Pritchett A. Describing airspace complexity: Airspace response to disturbances[J]. Journal of Guidance, Control and Dynamics, 2009, 132 (1):210-222.

[12] Zhao Y F, Zhang D, Yue R T, et al. Method to analyze air traffic situation based on air traffic complexity map[C]// 9th USA/Europe ATM R&D Seminar, Germany, Berlin, 2011.

[13] 张晨. 空中交通管理中的交通行为复杂性研究[D] . 南京:南京航空航天大学民航学院, 2011. [ZHANG C. Research on the complexity of the traffic behaviors in air traffic management[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, College of Civil Aviation, 2011.]

[14] 丛玮, 胡明华, 张晨. 复杂性指标体系的构建及精炼方法研究[J] . 交通运输系统工程与信息, 2012, 12(5): 130-134. [CONG W, HU M H, ZHANG C. Construction and refinement method of metrics system[J]. Journal of Transportation Systems Engineering and Information Technology, 2012, 12(5): 130-134.]

[15] Jelena D, Bernd L, Hartmut F. Air traffic control complexity as workload driver[J]. Transportation Research Part C, 2010, 18: 930-936.

[16] Lee S S, Lin J C. An accelerated K-means clustering algorithm using selection and erasure rules[J]. Journal of Zhejiang University (Computers & Electronics), 2012, 13 (10): 761-768.

[17] Zalik K R, Zalik B. Validity index for clusters of different sizes and densities[J]. Pattern Recognition Letters, 2011, 32(2): 221-234. 141