Air traffic density prediction using Bayesian ensemble graph attention network (BEGAN)

Xu, Qihang; Pang, Yutian; Liu, Yongming

doi:10.1016/j.trc.2023.104225

Transportation Research Part C: Emerging Technologies

2023

DOI: 10.1016/j.trc.2023.104225

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Air traffic density prediction using Bayesian ensemble graph attention network (BEGAN)

Qihang Xu

Yutian Pang

Yongming Liu

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Cited by 11 publications

References 47 publications

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Short-term multi-step-ahead sector-based traffic flow prediction based on the attention-enhanced graph convolutional LSTM network (AGC-LSTM)

Zhang,

Xu,

Zhang

et al. 2024

Neural Comput & Applic

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Accurate sector-based air traffic flow predictions are essential for ensuring the safety and efficiency of the air traffic management (ATM) system. However, due to the inherent spatial and temporal dependencies of air traffic flow, it is still a challenging problem. To solve this problem, some methods are proposed considering the relationship between sectors, while the complicated spatiotemporal dynamics and interdependencies between traffic flow of route segments related to the sector are not taken into account. To address this challenge, the attention-enhanced graph convolutional long short-term memory network (AGC-LSTM) model is applied to improve the short-term sector-based traffic flow prediction, in which spatial structures of route segments related to the sector are considered for the first time. Specifically, the graph convolutional networks (GCN)-LSTM network model was employed to capture spatiotemporal dependencies of the flight data, and the attention mechanism is designed to concentrate on the informative features from key nodes at each layer of the AGC-LSTM model. The proposed model is evaluated through a case study of the typical enroute sector in the central–southern region of China. The prediction results show that MAE reduces by 14.4% compared to the best performing GCN-LSTM model among the other five models. Furthermore, the study involves comparative analyses to assess the influence of route segment range, input and output sequence lengths, and time granularities on prediction performance. This study helps air traffic managers predict flight situations more accurately and avoid implementing overly conservative or excessively aggressive flow management measures for the sectors.

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Short-term multi-step-ahead sector-based traffic flow prediction based on the attention-enhanced graph convolutional LSTM network (AGC-LSTM)

Zhang,

Xu,

Zhang

et al. 2024

Neural Comput & Applic

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Artificial Intelligence in Aviation Safety: Systematic Review and Biometric Analysis

Demir,

Moslem,

Duleba

2024

Int J Comput Intell Syst

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Improved air traffic flow prediction in terminal areas using a multimodal spatial–temporal network for weather-aware (MST-WA) model

Zeng,

Hu,

Chen

et al. 2024

Advanced Engineering Informatics

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Air traffic density prediction using Bayesian ensemble graph attention network (BEGAN)

Cited by 11 publications

References 47 publications

Short-term multi-step-ahead sector-based traffic flow prediction based on the attention-enhanced graph convolutional LSTM network (AGC-LSTM)

Short-term multi-step-ahead sector-based traffic flow prediction based on the attention-enhanced graph convolutional LSTM network (AGC-LSTM)

Artificial Intelligence in Aviation Safety: Systematic Review and Biometric Analysis

Improved air traffic flow prediction in terminal areas using a multimodal spatial–temporal network for weather-aware (MST-WA) model

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