Volume 23 Issue 1
Feb.  2023
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ZHANG Hong-hai, LYU Wen-ying, WAN Jun-qiang, YANG Lei. Network modeling and evolution characteristics for air traffic risk situation in sectors[J]. Journal of Traffic and Transportation Engineering, 2023, 23(1): 222-241. doi: 10.19818/j.cnki.1671-1637.2023.01.017
Citation: ZHANG Hong-hai, LYU Wen-ying, WAN Jun-qiang, YANG Lei. Network modeling and evolution characteristics for air traffic risk situation in sectors[J]. Journal of Traffic and Transportation Engineering, 2023, 23(1): 222-241. doi: 10.19818/j.cnki.1671-1637.2023.01.017
shu

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

doi: 10.19818/j.cnki.1671-1637.2023.01.017
  • 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

  • Received Date: 2022-08-25
    Available Online: 2023-03-08
  • Publish Date: 2023-02-25
    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.

     

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