The Air Flow Operation Structure of Jing-Jin-Shi Multi-Airport System and Its Comparative Study

  • Jing Zhang ,
  • Zi Lu ,
  • Xinru Du ,
  • Xiaohui Du ,
  • Yujian Gao
Expand
  • 1.School of Resource and Environment Sciences,Hebei Normal University,Shijiazhuang 050024,China
    2.Hebei Key Laboratory of Environmental Change and Ecological Construction,Shijiazhuang 050024,China
    3.Yongding Branch School of High School Attached to Capital Normal University,Beijing 102308,China
    4.Department of Tourism,Hebei Normal University,Shijiazhuang 050024,China
    5.Transport Planning and Research Institute,Ministry of Transport,Beijing 100028,China
Zhang Jing (1992-), female, Handan City, Hebei Province, Master student. Research areas include aviation geography and development of airspace utilization. E-mail:zhangjingerv@163.com

Received date: 2020-05-16

  Revised date: 2020-11-03

  Online published: 2021-02-09

Supported by

the National Natural Science Foundation of China "The model and mechanisms of airspace configuration supported by data communication"(41671121)

Abstract

The real-time flight trajectory data of Jing-Jin-Shi multi-airport system was obtained by tracking and filtering, and an integrated detection framework for air flow operation structure of multi-airport system including trajectory network, trajectory cluster flow pattern, trajectory tube in crossing airspace and trajectory phase state was built. Through classification, grouping, layered and phased detection of adjacent flow, concurrent flow, multivariable flow and collective flow, it is found that: A small number of central trajectories can form a dominant air flow operation. The four groups of trajectory cluster flow patterns all last for a long time and there are differences in the concentration and dispersion of sliding windows. The spatio-temporal interaction of air traffic flow among the trajectory tube in crossing airspace is shown as a crossing relationship and also shares the same airspace unit at the same time. Besides, the phase state changes due to the influence of air flow participation and aviation network. Compared with the New York multi-airport system in the United States, the central trajectories are less and the flow direction area is narrower. Secondly, the trajectory cluster flow pattern and the trajectory tube in crossing airspace have longer concentration time and less variability, and the trajectory congestion is more likely and more frequent. The participation of Beijing Daxing International Airport will increase the central trajectories, reduce the peak value of sliding windows and also make the new crossing airspace. This research reveals the fundamental role of the aviation geographic market, which can provide decision-making for the dynamic airspace configuration and coordination of multi-airport system.

Cite this article

Jing Zhang , Zi Lu , Xinru Du , Xiaohui Du , Yujian Gao . The Air Flow Operation Structure of Jing-Jin-Shi Multi-Airport System and Its Comparative Study[J]. Advances in Earth Science, 2020 , 35(12) : 1281 -1291 . DOI: 10.11867/j.issn.1001-8166.2020.101

References

1 Zhang Yinuo, Lu Zi, Du Xinru, et al. Research on airspace resource allocation supported by spatiotemporal continuous data: Review and prospect [J]. Advances in Earth Science, 2019, 34(9): 912-921.
1 张一诺, 路紫, 杜欣儒, 等. 时空连续数据支持下的空域资源配置研究:评述与展望[J]. 地球科学进展, 2019, 34(9): 912-921.
2 Baumgartner M, Finger M. The Single European Sky gridlock: A difficult 10 year reform process [J]. Utilities Policy, 2014, 23(31): 289-301.
3 Connors M M, Mauro R, Statler I C. A survey methodology for measuring safety-related trends in the national airspace system [J]. The International Journal of Aviation Psychology, 2014, 24(4): 265-286.
4 Zhang Yinuo, Lu Zi, Ding Jianghui. Calculation of system delay elasticity of Beijing-Guangzhou Air Corridor with analysis of air flow operation structure [J]. Tropical Geography, 2020, 40(2): 194-205.
4 张一诺, 路紫, 丁疆辉. 京广空中廊道系统延误弹性测算与航空流运行结构分析[J]. 热带地理,2020, 40(2): 194-205.
5 Zhang Jing, Lu Zi, Dong Yaqing. Dynamic analysis of air passenger flow in Jing-Jin-Shi MAS terminal airspace and its application prospects [J]. Geography and Geo-Information Science, 2019, 35(5): 73-79,117.
5 张菁, 路紫, 董雅晴. 京津石MAS终端空域航空流动态分析及其应用展望[J]. 地理与地理信息科学, 2019, 35(5): 73-79,117.
6 Wang Chengjin, Wang Wei, Wang Jiao'e. Pattern of schedule network in hub airports: From the view of airlines reorganization [J]. Geographical Research, 2015, 34(6): 1 029-1 043.
6 王成金, 王伟, 王姣娥. 基于航空公司重组的枢纽机场航班配置网络演变——以北京、上海和广州为例[J]. 地理研究, 2015, 34(6): 1 029-1 043.
7 Du Chao, Wang Jiao'e. Spatial pattern of China Southern Airlines' network and its market coverage [J]. Geographical Research, 2015, 34(7): 1 319-1 330.
7 杜超, 王姣娥. 南方航空网络空间格局及市场范围[J]. 地理研究, 2015, 34(7): 1 319-1 330.
8 Wang Jiao'e, Mo Huihui, Jin Fengjun. Spatial structural characteristics of chinese aviation network based on complex network theory [J]. Acta Geographica Sinica, 2009, 64(8): 899-910.
8 王姣娥, 莫辉辉, 金凤君. 中国航空网络空间结构的复杂性[J]. 地理学报, 2009, 64(8): 899-910.
9 Sidiropoulos S, Majumdar A, Han K. A framework for the optimization of terminal airspace operations in multi-airport systems[J]. Transportation Research Part B: Methodological, 2018, 110(4): 160-187.
10 Zhang Honghai, Xu Yan, Zhang Zheming, et al. Air traffic flow parameter model and simulation for airport terminal area [J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(6): 58-64.
10 张洪海, 许炎, 张哲铭, 等. 终端区空中交通流参数模型与仿真[J]. 交通运输系统工程与信息, 2014, 14(6): 58-64.
11 Mur?a M C R, Hansman R J, Li L, et al. Flight trajectory data analytics for characterization of air traffic flows: A comparative analysis of terminal area operations between New York, Hong Kong and Sao Paulo[J]. Transportation Research Part C: Emerging Technologies, 2018, 26(97): 324-347.
12 Gao Wei, Lu Zi, Dong Yaqing. Idenification of anomalous change of air traffic flow based on individual moving trajectory-sliding window: A case study of Beijing-Shanghai Air Corridor [J]. Geography and Geo-Information Science, 2019, 35(6): 66-72.
12 高伟, 路紫, 董雅晴. 个体移动轨迹—滑动窗口方法与航空流异常变化识别——以京沪空中廊道为例[J]. 地理与地理信息科学, 2019, 35(6): 66-72.
13 Xie Daoyi, Hu Minghua, Xie Hua, et al. Analysis of fluctuations in air-route network flow [J]. Journal of East China Jiaotong University, 2017, 34(1): 73-78.
13 谢道仪, 胡明华, 谢华, 等. 航路网络流量波动行为分析[J]. 华东交通大学学报, 2017, 34(1): 73-78.
14 Xu Yan, Zhang Honghai, Yang Lei, et al. Analysis of air traffic flow characteristics in airport terminal area based on observed data [J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(1): 205-211.
14 许炎, 张洪海, 杨磊, 等. 基于实测数据的终端区空中交通流特性分析 [J]. 交通运输系统工程与信息, 2015, 15(1): 205-211.
15 Mur?a M C R, DeLaura R, Hansman R J, et al. Trajectory clustering and classification for characterization of air traffic flows [C]//16th AIAA Aviation Technology, Integration, and Operations Conference. Washington DC, 2016: 1-15.
16 He Z, Yu C. Clustering stability-based evolutionary K-Means [J]. Soft Computing, 2019, 23(1): 305-321.
17 Andrienko G, Andrienko N, Fuchs G, et al. Clustering trajectories by relevant parts for air traffic analysis [J]. IEEE Transactions on Visualization and Computer Graphics, 2017, 24(1): 34-44.
18 Tang J, Alam S, Lokan C, et al. A multi-objective approach for dynamic airspace sectorization using agent based and geometric models [J]. Transportation Research Part C, 2012, 21(1): 89-121.
19 De Neufville R. Management of multi-airport systems: A development strategy [J]. Journal of Air Transport Management, 1995, 2(2): 99-110.
20 Loo B P Y. Passengers' airport choice within Multi-Airport Regions (MARs): Some insights from a stated preference survey at Hong Kong International Airport [J]. Journal of Transport Geography, 2008, 16(2): 117-125.
21 Postorino M N, Praticò F G. An application of the Multi-Criteria Decision-Making analysis to a regional multi-airport system [J]. Research in Transportation Business & Management, 2012, 2(4): 44-52.
22 Griffin R. State aid, the growth of low-cost carriers in the European Union, and the impact of the 2005 guidelines on financing of airports and start-up aid to airlines departing from regional airports [J]. Office of Scientific & Technical Information Technical Reports, 2006, 78(1):158-166.
23 Yang Z, Yu S, Notteboom T. Airport location in Multiple Airport Regions (MARs): The role of land and airside accessibility [J]. Journal of Transport Geography, 2016, 52: 98-110.
24 Sidiropoulos S, Han K, Majumdar A, et al. Robust identification of air traffic flow patterns in Metroplex terminal areas under demand uncertainty [J]. Transportation Research Part C: Emerging Technologies, 2017, 15(75): 212-227.
25 Sun X, Wandelt S, Hansen M, et al. Multiple airport regions based on inter-airport temporal distances [J]. Transportation Research, 2017, 101 (5): 84-98.
26 Li M Z, Ryerson M S. A data-driven approach to modeling high-density terminal areas: A scenario analysis of the new Beijing, China airspace [J]. Chinese Journal of Aeronautics, 2017, 30(2): 538-553.
27 Wandelt S, Sun X. Efficient compression of 4D-trajectory data in air traffic management [J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 16(2): 844-853.
28 Sun J, Ellerbroek J, Hoekstra J. Flight extraction and phase identification for large automatic dependent surveillance-broadcast datasets [J]. Journal of Aerospace Computing, Information and Communication, 2017, 14(10): 1-6.
29 Kistan T, Gardi A, Sabatini R, et al. An evolutionary outlook of air traffic flow management techniques [J]. Progress in Aerospace Sciences, 2017, 88(1): 15-42.
30 Campanelli B, Fleurquin P, Arranz A, et al. Comparing the modeling of delay propagation in the US and European air traffic networks [J]. Journal of Air Transport Management, 2016, 56(8): 12-18.
31 Brrnhart C, Bertsimas D, Caramanis C, et al. Equitable and efficient coordination in traffic flow management [J]. Transportation Science, 2012, 46(2): 262-280.
32 Clark K L, Bhatia U, Kodra E A, et al. Resilience of the US national airspace system airport network [J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(12): 3 785-3 794.
33 Sun J, Ellerbroek J, Hoekstra J. Large-scale flight phase identification from ads-b data using machine learning methods [C]//7th International Conference on Research in Air Transportation. Philadelphia, USA. 2016.
34 Derudder B, Devriendt L, Witlox F. A spatial analysis of multiple airport cities [J]. Journal of Transport Geography, 2010, 18(3): 345-353.
35 Liu Yonggang, Yang Yi. Analysis on the transition optimization between terminal airspace and en-route airspace [J]. Air Traffic, 2015(10):5-8.
35 刘永刚, 杨毅. 对优化终端区与航路航线网络衔接的思考 [J]. 空中交通, 2015 (10):5-8.
36 Zhang Shengrun, Zheng Hailong, Li Tao, et al. Research on congestion spillover effects of international transfer traffic on hub airports [J]. Geographical Research, 2019, 38(11): 2 716-2 729.
36 张生润, 郑海龙, 李涛, 等. 枢纽机场的国际中转客流拥堵溢出效应研究[J]. 地理研究, 2019, 38(11): 2 716-2 729.
37 Du Xinru, Lu Zi, Dong Yaqing, et al. The heat airspace cloud map in terminal airspace of airports based on air passenger flow and a case study in Beijing international airport [J]. Advances in Earth Science, 2019, 34(8): 879-888.
37 杜欣儒, 路紫, 董雅晴, 等. 机场终端空域航空流量热区云图模型及其北京首都国际机场案例研究[J]. 地球科学进展, 2019, 34(8): 879-888.
38 Xu Xiaohao,Li Shanmei. Research on identification and prediction methods of air traffic congestion [J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(8):2 753-2 763.
38 徐肖豪, 李善梅. 空中交通拥挤的识别与预测方法研究[J]. 航空学报, 2015, 36(8): 2 753-2 763.
Outlines

/