A Dynamic Multi-Graph Convolutional Spatial-Temporal Network for Airport Arrival Flow Prediction
In air traffic systems, aircraft trajectories between airports are monitored by the radar networking system forming dynamic air traffic flow. Accurate airport arrival flow prediction is significant in implementing large-scale intelligent air traffic flow management. Despite years of studies to impro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/12/5/395 |
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| author | Yunyang Huang Hongyu Yang Zhen Yan |
| author_facet | Yunyang Huang Hongyu Yang Zhen Yan |
| author_sort | Yunyang Huang |
| collection | DOAJ |
| description | In air traffic systems, aircraft trajectories between airports are monitored by the radar networking system forming dynamic air traffic flow. Accurate airport arrival flow prediction is significant in implementing large-scale intelligent air traffic flow management. Despite years of studies to improve prediction precision, most existing methods only focus on a single airport or simplify the traffic network as a static and simple graph. To mitigate this shortage, we propose a hybrid neural network method, called Dynamic Multi-graph Convolutional Spatial-Temporal Network (DMCSTN), to predict network-level airport arrival flow considering the multiple operation constraints and flight interactions among airport nodes. Specifically, in the spatial dimension, a novel dynamic multi-graph convolutional network is designed to adaptively model the heterogeneous and dynamic airport networks. It enables the proposed model to dynamically capture informative spatial correlations according to the input traffic features. In the temporal dimension, an enhanced self-attention mechanism is utilized to mine the arrival flow evolution patterns. Experiments on a real-world dataset from an ATFM system validate the effectiveness of DMCSTN for arrival flow forecasting tasks. |
| format | Article |
| id | doaj-art-58f1d1089caf4e378318dec124d56d48 |
| institution | Kabale University |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-58f1d1089caf4e378318dec124d56d482025-08-20T03:47:49ZengMDPI AGAerospace2226-43102025-04-0112539510.3390/aerospace12050395A Dynamic Multi-Graph Convolutional Spatial-Temporal Network for Airport Arrival Flow PredictionYunyang Huang0Hongyu Yang1Zhen Yan2College of Computer Science, Sichuan University, Chengdu 610065, ChinaNational Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610065, ChinaThe State Key Laboratory of Air Traffic Management System, Nanjing 210000, ChinaIn air traffic systems, aircraft trajectories between airports are monitored by the radar networking system forming dynamic air traffic flow. Accurate airport arrival flow prediction is significant in implementing large-scale intelligent air traffic flow management. Despite years of studies to improve prediction precision, most existing methods only focus on a single airport or simplify the traffic network as a static and simple graph. To mitigate this shortage, we propose a hybrid neural network method, called Dynamic Multi-graph Convolutional Spatial-Temporal Network (DMCSTN), to predict network-level airport arrival flow considering the multiple operation constraints and flight interactions among airport nodes. Specifically, in the spatial dimension, a novel dynamic multi-graph convolutional network is designed to adaptively model the heterogeneous and dynamic airport networks. It enables the proposed model to dynamically capture informative spatial correlations according to the input traffic features. In the temporal dimension, an enhanced self-attention mechanism is utilized to mine the arrival flow evolution patterns. Experiments on a real-world dataset from an ATFM system validate the effectiveness of DMCSTN for arrival flow forecasting tasks.https://www.mdpi.com/2226-4310/12/5/395airport arrival flow predictiondeep learningspatial-temporal dependenciesmulti-graph fusiongraph neural network |
| spellingShingle | Yunyang Huang Hongyu Yang Zhen Yan A Dynamic Multi-Graph Convolutional Spatial-Temporal Network for Airport Arrival Flow Prediction Aerospace airport arrival flow prediction deep learning spatial-temporal dependencies multi-graph fusion graph neural network |
| title | A Dynamic Multi-Graph Convolutional Spatial-Temporal Network for Airport Arrival Flow Prediction |
| title_full | A Dynamic Multi-Graph Convolutional Spatial-Temporal Network for Airport Arrival Flow Prediction |
| title_fullStr | A Dynamic Multi-Graph Convolutional Spatial-Temporal Network for Airport Arrival Flow Prediction |
| title_full_unstemmed | A Dynamic Multi-Graph Convolutional Spatial-Temporal Network for Airport Arrival Flow Prediction |
| title_short | A Dynamic Multi-Graph Convolutional Spatial-Temporal Network for Airport Arrival Flow Prediction |
| title_sort | dynamic multi graph convolutional spatial temporal network for airport arrival flow prediction |
| topic | airport arrival flow prediction deep learning spatial-temporal dependencies multi-graph fusion graph neural network |
| url | https://www.mdpi.com/2226-4310/12/5/395 |
| work_keys_str_mv | AT yunyanghuang adynamicmultigraphconvolutionalspatialtemporalnetworkforairportarrivalflowprediction AT hongyuyang adynamicmultigraphconvolutionalspatialtemporalnetworkforairportarrivalflowprediction AT zhenyan adynamicmultigraphconvolutionalspatialtemporalnetworkforairportarrivalflowprediction AT yunyanghuang dynamicmultigraphconvolutionalspatialtemporalnetworkforairportarrivalflowprediction AT hongyuyang dynamicmultigraphconvolutionalspatialtemporalnetworkforairportarrivalflowprediction AT zhenyan dynamicmultigraphconvolutionalspatialtemporalnetworkforairportarrivalflowprediction |