An operational spatiotemporal prediction framework for sector congestion in air traffic management
- 1Air Traffic Management Institute, Civil Aviation Flight University of China, Guanghan, Sichuan, China
- 2Operation Management Center, Civil Aviation Administration of China Air Traffic Management Bureau, Beijing, China
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.
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BibTeX
@article{chen2026operational,
title = {{An operational spatiotemporal prediction framework for sector congestion in air traffic management}},
author = {Huaqun Chen and Zhenyu Gao and Xiaoyu Yan and Yuehui Yi and Jintao Gong and Lin Wu},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0355927},
url = {https://doi.org/10.1371/journal.pone.0355927},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - An operational spatiotemporal prediction framework for sector congestion in air traffic management
AU - Huaqun Chen
AU - Zhenyu Gao
AU - Xiaoyu Yan
AU - Yuehui Yi
AU - Jintao Gong
AU - Lin Wu
PY - 2026
DA - 2026/10/27
JO - PLOS One
DO - 10.1371/journal.pone.0355927
UR - https://doi.org/10.1371/journal.pone.0355927
AB - 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.
SN - 1932-6203
ER -