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Air Traffic Flow Pattern Recognition and Analysis in Terminal Area Based on the Geodesic Distance

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Abstract

An accurate recognition method of the air traffic flow pattern is proposed based on historical flight trajectory data to understand the spatial distribution of air traffic flow and improve the airspace utilization in the terminal area. Due to the high dimensionality of flight trajectory data, we establish a trajectory similarity model based on the geodesic distance and use an improved spectral clustering algorithm to classify the flight trajectory sample data. An improved algorithm based on the minimum spanning tree is presented to extract the skeleton similarity between the tracks and obtain the model of the prevalent traffic flow. The experimental results show that the method can accurately divide 1070 flight paths into 5 categories and extract 7 prevalent traffic flows, exhibiting strong robustness to abnormal trajectories and noise.

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Acknowledgements

his work was supported by the Fundamental Research Funds for the Central Universities and CAUC special fund under Grant 3122019129.

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Authors and Affiliations

  1. College of Air Traffic Management, Civil Aviation University of China, Tianjin, 300300, China

    Zhaoyue Zhang, Zhisen Wang & Zhe Cui

  2. School of Aeronautics, Northwestern Polytechnical University, Xi’an, 710072, China

    Zhaoyue Zhang

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  1. Zhaoyue Zhang
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  2. Zhisen Wang
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  3. Zhe Cui
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Correspondence to Zhaoyue Zhang.

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Zhang, Z., Wang, Z. & Cui, Z. Air Traffic Flow Pattern Recognition and Analysis in Terminal Area Based on the Geodesic Distance. Mobile Netw Appl 27, 752–766 (2022). https://doi.org/10.1007/s11036-021-01905-3

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