{
  "abstract": "En-route sector congestion poses a critical challenge to air traffic safety, operational efficiency, and resource-efficient airspace operation in high-density airspace. Because sector operations are spatially networked, locally emerging congestion may propagate through adjacent or upstream grid areas, leading to large-scale delays, increased fuel consumption, and elevated controller workload. This study proposes an operational spatiotemporal prediction framework for 15-minute-ahead sector congestion warning. First, processed grid-level operational states are converted into three congestion levels using K-means++ clustering based on six direct observable operational-state indicators. Second, a hybrid GNN-BiLSTM-Cross-Attention model predicts future congestion levels and local T2 hotspots from historical 5-minute grid-window sequences, without using future observed features. Third, SHAP/SHEP-style post-hoc grouped attribution is applied after inference to distinguish external triggering risk from congestion-propagation risk and to decompose external contributions from weather, restricted areas, controller workload, and wake intensity. A case study using 2023 processed grid-level operational data from the ZGGGAR15 high-altitude en-route sector achieves TEST@VAL Weighted F1 = 0.9773, L2-F1 = 0.9602, Precision = 0.9545, Recall = 0.9660, and AP = 0.9767 for rare high-congestion warning.The results indicate that the proposed framework provides accurate and spatially interpretable predictions with potential decision-support value for proactive air traffic flow management.",
  "abstract_shown": true,
  "affiliations": [
    "Air Traffic Management Institute, Civil Aviation Flight University of China, Guanghan, Sichuan, China",
    "Operation Management Center, Civil Aviation Administration of China Air Traffic Management Bureau, Beijing, China"
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        0
      ],
      "name": "Huaqun Chen"
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    {
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        0
      ],
      "name": "Zhenyu Gao"
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    {
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        1
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      "name": "Xiaoyu Yan",
      "orcid": "0009-0008-1531-6696"
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    {
      "affs": [
        0
      ],
      "name": "Yuehui Yi"
    },
    {
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        0
      ],
      "name": "Jintao Gong"
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    {
      "affs": [
        0
      ],
      "name": "Lin Wu"
    }
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  "doi": "10.1371/journal.pone.0355927",
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  "title": "An operational spatiotemporal prediction framework for sector congestion in air traffic management",
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