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Research on Rapid Congestion Identification Method Based on TSNE-FCM and LightGBM

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  • Cheng Deng

    (College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Qiqian Zhang

    (College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Honghai Zhang

    (College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Jingyu Li

    (College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Changyuan Ning

    (College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

Abstract

The terminal area is a convergence point for inbound and outbound traffic, and it is characterized by a complex airspace structure and high traffic density. It is an area that frequently experiences flight congestion and ground delays. A system capable of the intelligent, reliable, timely, and accurate identification of air traffic congestion for air–ground coupled flight flow constitutes a key technology with respect to unlocking the potential capacity of the terminal area, mitigating traffic congestion, and assisting air-traffic-control-related decision making. Therefore, this article aims to extract and analyze the multi-scale and multi-dimensional evaluation indicators of air–ground coupled flight flow congestion, use the TSNE-FCM algorithm to classify congestion levels, and, based on this work, construct a real-time and fast congestion identification model using the LightGBM algorithm. The case study analyzed China Baiyun Airport (CAN), and the experimental results indicate the following: (1) The congestion level classification achieved using the TSNE-FCM algorithm is superior to that achieved using the FCM algorithm. Furthermore, flight delays predominantly occur in slightly congested and congested states. (2) The congestion identification model based on LightGBM outperforms the XGBoost, RandomForest, and ExtraTree models. The macro-average and micro-average AUC curve areas for the LightGBM model were 0.96 and 0.96, respectively. The LightGBM model demonstrates excellent performance and is suitable for identifying congestion levels in practical engineering applications.

Suggested Citation

  • Cheng Deng & Qiqian Zhang & Honghai Zhang & Jingyu Li & Changyuan Ning, 2023. "Research on Rapid Congestion Identification Method Based on TSNE-FCM and LightGBM," Sustainability, MDPI, vol. 15(14), pages 1-16, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11322-:d:1198638
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    References listed on IDEAS

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    1. Vaaben, Bo & Larsen, Jesper, 2015. "Mitigation of airspace congestion impact on airline networks," Journal of Air Transport Management, Elsevier, vol. 47(C), pages 54-65.
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