扇区空中交通风险态势网络建模与演化特征

张洪海; 吕文颖; 万俊强; 杨磊

doi:10.19818/j.cnki.1671-1637.2023.01.017

扇区空中交通风险态势网络建模与演化特征

doi: 10.19818/j.cnki.1671-1637.2023.01.017

张洪海^{1, 2,},
吕文颖¹,
万俊强¹,
杨磊¹

1.
南京航空航天大学民航学院，江苏南京 211106
2.
南京航空航天大学通用航空与飞行学院，江苏南京 211106

基金项目:

国家重点研发计划 2018YFE0208700

国家自然科学基金项目 71971114

详细信息

作者简介:
张洪海(1979-)，男，山东菏泽人，南京航空航天大学教授，工学博士，从事空中交通管理、通用航空及无人机管控研究

中图分类号: V355
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- 被引次数: 0
出版历程
- 收稿日期: 2022-08-25
- 网络出版日期: 2023-03-08
- 刊出日期: 2023-02-25

Network modeling and evolution characteristics for air traffic risk situation in sectors

1.
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, China
2.
College of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, China

Funds:

National Key Research and Development Program of China 2018YFE0208700

National Natural Science Foundation of China 71971114

More Information

Author Bio:
ZHANG Hong-hai(1979-), male, professor, PhD, zhh0913@163.com

摘要

摘要: 为了准确感知空域内空中交通运行态势，提升飞行运行效能，研究了扇区空中交通风险态势的网络建模方法和演化特征，基于实测数据验证了方法的可行性与有效性；在扇区内以活跃航空器为网络节点，依据航空器位置偏差下的冲突关系构建连边，建立空中交通风险态势网络，利用连通分量的概念定义并识别网络内的航空器集群；基于航空器集群特征构建状态向量，进一步划分空中交通风险态势模式；以分类后的态势等级时序为基础，针对各模式的样本进行持续时间分析及建模，讨论了单一态势模式的生存特性与多模式间转移行为的偏好水平；为了验证方法的有效性，以广州管制AR05号扇区的实际数据为样本展开分析。研究结果表明：低风险至高风险模式的生存特性差异明显，平均生存周期分别为82.49、118.11、75.77、90.51 s，中等风险模式是估计生命周期最长且生存率最高的模式；在模式迁移的过程中，集群数目与规模相对简单的风险模式主要表现为前向可达性较高，而回迁及跃迁概率低于0.05；复杂程度较高的风险模式则表现出了较为明显的回迁行为，且概率会随着时间步长的增加(30、60、90、120 s)逐渐趋于稳定。可见，建立的空中交通风险态势网络模型能够较好反映空中交通风险态势信息，提出的演化分析方法可以为空中交通运行提供有益参考，从而发掘空中交通演变的科学规律。
- 空中交通管理 /
- 网络建模 /
- 演化分析 /
- 航空器集群 /
- 风险模式 /
- 生存时间
Abstract: To accurately capture the air traffic operation situation in the airspace and improve the operation efficiency of flight, the methods of network modeling for air traffic risk situation in sectors and its evolution characteristics were studied. The feasibility and effectiveness of the methods were verified based on the measured data. In the sectors, active aircrafts were abstracted as nodes, and the edges were established based on the conflict relationship under their position deviations to help build the air traffic situation network. The aircraft clusters were defined and detected by using the concept of connected components within the network. The state vector was established based on the characteristics of aircraft clusters to further classify the risk patterns of air traffic. On the basis of the classified time sequence of situation class, the duration sample for each pattern was analyzed and modelled. The survival characteristics for a single pattern and the preference level of transition among multiple patterns were discussed. To verify the validity of the proposed method, the data about Guangzhou control sector AR05 were used as the sample for analysis. Analysis results show that the survival characteristics of the low risk to high risk pattern differ significantly with mean life cycles of 82.49, 118.11, 75.77 and 90.51 s, respectively. Among them the medium risk pattern is the one with the longest estimated life cycle and the highest survival rate. In the process of pattern transition, the risk patterns with relatively simple and smaller size of clusters mainly show a higher forward accessibility, while the backward and leap probabilities are lower than 0.05. The more complex ones show more obvious backward transition behavior, and the probabilities gradually stabilize with increasing time steps (30, 60, 90 and 120 s). So, the established network model can well reflect the information on the risk situation of air traffic, and the proposed evolution analysis method can give valuable insights into air traffic operation, thus identifying the scientific laws of air traffic evolution.
- air traffic management /
- network modeling /
- evolution analysis /
- aircraft cluster /
- risk pattern /
- duration

HTML全文

图 1 研究框架

Figure 1. Research framework

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图 2 导航规范服从的正态分布

Figure 2. Normal distributions of navigation specifications

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图 3 等效区域

Figure 3. Equivalence area

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图 4 空中交通风险态势时变网络

Figure 4. Time varying networks of air traffic risk situation

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图 5 LPA和WA的集群识别结果

Figure 5. Clusters identification results of LPA and WA

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图 6 不同时刻下的连通分量

Figure 6. Connected components at different times

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图 7 广州管制AR05扇区的空域结构与航迹

Figure 7. Airspace structure and flight path of Guangzhou control sector AR05

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图 8 轮廓系数与聚类数目

Figure 8. Silhouette coefficients and numbers of clusters

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图 9 风险模式的状态向量

Figure 9. State vectors of risk patterns

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图 10 风险模式的持续时间及出现频次

Figure 10. Durations and frequencies of risk patterns

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图 11 风险模式的小时占比

Figure 11. Hourly percentages of risk patterns

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图 12 低风险模式持续时间分布

Figure 12. Distributions of duration for low risk pattern

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图 13 中等风险模式持续时间分布

Figure 13. Distributions of duration for medium risk pattern

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图 14 趋高风险模式持续时间分布

Figure 14. Distributions of duration for medium-high risk pattern

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图 15 高风险模式持续时间分布

Figure 15. Distributions of duration for high risk pattern

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图 16 风险模式的生命周期估计

Figure 16. Estimated life cycles of risk patterns

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图 17 风险模式的生存率

Figure 17. Survival rates of risk patterns

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图 18 风险模式生存时间的百分位数

Figure 18. Percentile values of survival time in risk patterns

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图 19 风险模式转移

Figure 19. Transition among risk patterns

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图 20 风险模式的转移概率

Figure 20. Transition probabilities of risk patterns

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图 21 不同时间步长下的风险模式转移概率

Figure 21. Transition probabilities of risk patterns under different time steps

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图 22 不同小时时段内的风险模式转移概率

Figure 22. Transition probabilities of risk patterns under different hourly time periods

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表 1 ADS-B数据格式

Table 1. Formats of ADS-B data

呼号	经度/(°)	纬度/(°)	高度/m	速度/(km·h^-1)	…	时刻
SKW4310	-104.685	38.826	2 324.10	333.35	…	22:25:09
SKW4310	-104.516	38.862	3 581.39	629.67	…	22:27:39
SKW4310	-104.474	38.592	5 577.83	685.23	…	22:30:21
SKW4310	-104.500	38.323	7 132.31	740.79	…	22:32:57

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表 2 不同时刻下状态向量及风险模式

Table 2. State vectors and risk patterns at different times

时间	O(t)		风险模式	时间	O(t)		风险模式
时间	N(t)	L(t)	风险模式	时间	N(t)	L(t)	风险模式
8:00:00	1	1	低风险模式	12:09:00	3	8	趋高风险模式
8:00:10	2	2	低风险模式	12:09:10	3	8	趋高风险模式
8:00:20	2	2	低风险模式	12:09:20	3	8	趋高风险模式
8:00:30	2	2	低风险模式	12:09:30	3	8	趋高风险模式
8:00:40	3	2	中等风险模式	12:09:40	3	9	高风险模式
8:00:50	3	2	中等风险模式	12:09:50	3	9	高风险模式
							

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表 3 生存曲线检验结果

Table 3. Test results of survival curves

日期	Log-Rank	Breslow	Tarone-Ware
5月1日	0.000 005 8	0.001 497 0	0.000 148 3
5月2日	0.002 527 1	0.000 105 2	0.000 241 3
5月3日	0.000 301 2	0.014 227 5	0.003 898 7
5月4日	0.024 056 7	0.000 488 9	0.001 110 4

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