Air Traffic Flow Prediction with Spatiotemporal Knowledge Distillation Network
Accurate air traffic flow prediction assists controllers formulate control strategies in advance and alleviate air traffic congestion, which is important to flight safety. While existing works have made significant efforts in exploring the high dynamics and heterogeneous interactions of historical a...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2024/4349402 |
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| _version_ | 1841524301736443904 |
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| author | Zhiqi Shen Kaiquan Cai Quan Fang Xiaoyan Luo |
| author_facet | Zhiqi Shen Kaiquan Cai Quan Fang Xiaoyan Luo |
| author_sort | Zhiqi Shen |
| collection | DOAJ |
| description | Accurate air traffic flow prediction assists controllers formulate control strategies in advance and alleviate air traffic congestion, which is important to flight safety. While existing works have made significant efforts in exploring the high dynamics and heterogeneous interactions of historical air traffic flow, two key challenges still remain. (1) The transfer patterns of air traffic are intricate, subject to numerous constraints and limitations such as controllers, flight regulations, and other regulatory factors. Relying solely on mining historical traffic evolution patterns makes it difficult to accurately predict the constrained air traffic flow. (2) Weather conditions exert a substantial influence on air traffic, making it exceptionally difficult to simulate the impact of external factors (such as thunderstorms) on the evolution of air traffic flow patterns. To address these two challenges, we propose a Spatiotemporal Knowledge Distillation Network (ST-KDN) for air traffic flow prediction. Firstly, recognizing the inherent future insights embedded within flight plans, we develop a “teacher-student” distillation model. This model leverages the prior knowledge of upstream-downstream migration patterns and future air traffic trends inherent in flight plans. Subsequently, to model the influence of external factors and predict air traffic flow disturbed by thunderstorm weather, we propose a student network based on the “parallel-fusion” structure. Finally, employing a feature-based knowledge distillation approach to integrate prior knowledge from flight plans and extract meteorological features, our method can accurately capture complex and constrained spatiotemporal dependencies in air traffic and explicitly model the impact of weather on air traffic flow. Experimental results on real-world flight data demonstrate that our method can achieve better prediction performance than other state-of-the-art comparison methods, and the advantages of the proposed method are particularly prominent in modeling the complicated transfer pattern of air traffic and inferring nonrecurrent flow patterns. |
| format | Article |
| id | doaj-art-c903265b7c0147e2b45d6e95aad5722b |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-c903265b7c0147e2b45d6e95aad5722b2025-02-03T07:23:23ZengWileyJournal of Advanced Transportation2042-31952024-01-01202410.1155/2024/4349402Air Traffic Flow Prediction with Spatiotemporal Knowledge Distillation NetworkZhiqi Shen0Kaiquan Cai1Quan Fang2Xiaoyan Luo3School of Electronic Information EngineeringSchool of Electronic Information EngineeringSchool of Artificial IntelligenceSchool of AstronauticsAccurate air traffic flow prediction assists controllers formulate control strategies in advance and alleviate air traffic congestion, which is important to flight safety. While existing works have made significant efforts in exploring the high dynamics and heterogeneous interactions of historical air traffic flow, two key challenges still remain. (1) The transfer patterns of air traffic are intricate, subject to numerous constraints and limitations such as controllers, flight regulations, and other regulatory factors. Relying solely on mining historical traffic evolution patterns makes it difficult to accurately predict the constrained air traffic flow. (2) Weather conditions exert a substantial influence on air traffic, making it exceptionally difficult to simulate the impact of external factors (such as thunderstorms) on the evolution of air traffic flow patterns. To address these two challenges, we propose a Spatiotemporal Knowledge Distillation Network (ST-KDN) for air traffic flow prediction. Firstly, recognizing the inherent future insights embedded within flight plans, we develop a “teacher-student” distillation model. This model leverages the prior knowledge of upstream-downstream migration patterns and future air traffic trends inherent in flight plans. Subsequently, to model the influence of external factors and predict air traffic flow disturbed by thunderstorm weather, we propose a student network based on the “parallel-fusion” structure. Finally, employing a feature-based knowledge distillation approach to integrate prior knowledge from flight plans and extract meteorological features, our method can accurately capture complex and constrained spatiotemporal dependencies in air traffic and explicitly model the impact of weather on air traffic flow. Experimental results on real-world flight data demonstrate that our method can achieve better prediction performance than other state-of-the-art comparison methods, and the advantages of the proposed method are particularly prominent in modeling the complicated transfer pattern of air traffic and inferring nonrecurrent flow patterns.http://dx.doi.org/10.1155/2024/4349402 |
| spellingShingle | Zhiqi Shen Kaiquan Cai Quan Fang Xiaoyan Luo Air Traffic Flow Prediction with Spatiotemporal Knowledge Distillation Network Journal of Advanced Transportation |
| title | Air Traffic Flow Prediction with Spatiotemporal Knowledge Distillation Network |
| title_full | Air Traffic Flow Prediction with Spatiotemporal Knowledge Distillation Network |
| title_fullStr | Air Traffic Flow Prediction with Spatiotemporal Knowledge Distillation Network |
| title_full_unstemmed | Air Traffic Flow Prediction with Spatiotemporal Knowledge Distillation Network |
| title_short | Air Traffic Flow Prediction with Spatiotemporal Knowledge Distillation Network |
| title_sort | air traffic flow prediction with spatiotemporal knowledge distillation network |
| url | http://dx.doi.org/10.1155/2024/4349402 |
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