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<p>Traffic network delays seriously affect the air transportation system’s safety, economy, and efficiency, and have always been a global concern. Flight delays usually propagate within airport networks, causing subsequent flights to be delayed. However, existing works lack in considering network causality, and the incorporation of emerging large language models (LLMs). Thus, this paper endeavours to examine the literature on network delay prediction that combines different background knowledge with journal paper publishing data. Particularly, the network delay prediction methods are categorized into four aspects: classic methods without explicit network topology modelling, traditional explicit network-based prediction methods, emerging deep learning methods, and the application of LLMs in transportation. Classic methods without explicit network topology modelling, including statistical analysis, operations research, traditional machine learning and causal inference without network structures, offer interpretable baselines but fail to capture the complexity and nonlinearity of air traffic systems. Traditional explicit network-based prediction methods often approach air traffic systems through frameworks such as complex networks and queuing theory, with an increasing focus on causal relationship analysis. However, these methods fall short in capturing the spatiotemporal dependencies of network delays, particularly in modelling spatiotemporal causality. In contrast, emerging deep learning methods have advanced significantly, enabling the construction of spatiotemporal causal networks and improving the accuracy of network delay prediction. In addition, some future trends are analyzed. It is concluded that graph neural networks with causality and emerging deep learning methods (e.g., spatiotemporal GCN) are identified as essential directions. Moreover, a conceptual AirTraffic LLM is suggested via a novel Spatial-Temporal Causal Large Language Model (STC-LLM) framework for high-precision flight delay prediction, which requires further experimental validation and real-world testing. Nevertheless, issues such as data privacy, model opacity, and high computational costs must be carefully addressed when applying LLMs. Finally, the findings are expected to enhance understanding of delay propagation among researchers, practitioners, and policymakers, while providing insights and guidance to airports, airlines, and air traffic control.</p>

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A review of network delay prediction and advances in large language models for air traffic

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月16日

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  • Mengyuan Sun,
  • Yong Tian,
  • Jiangchen Li,
  • Cheng-Lung Wu,
  • Liqun Peng,
  • Shucai Xu

摘要

Traffic network delays seriously affect the air transportation system’s safety, economy, and efficiency, and have always been a global concern. Flight delays usually propagate within airport networks, causing subsequent flights to be delayed. However, existing works lack in considering network causality, and the incorporation of emerging large language models (LLMs). Thus, this paper endeavours to examine the literature on network delay prediction that combines different background knowledge with journal paper publishing data. Particularly, the network delay prediction methods are categorized into four aspects: classic methods without explicit network topology modelling, traditional explicit network-based prediction methods, emerging deep learning methods, and the application of LLMs in transportation. Classic methods without explicit network topology modelling, including statistical analysis, operations research, traditional machine learning and causal inference without network structures, offer interpretable baselines but fail to capture the complexity and nonlinearity of air traffic systems. Traditional explicit network-based prediction methods often approach air traffic systems through frameworks such as complex networks and queuing theory, with an increasing focus on causal relationship analysis. However, these methods fall short in capturing the spatiotemporal dependencies of network delays, particularly in modelling spatiotemporal causality. In contrast, emerging deep learning methods have advanced significantly, enabling the construction of spatiotemporal causal networks and improving the accuracy of network delay prediction. In addition, some future trends are analyzed. It is concluded that graph neural networks with causality and emerging deep learning methods (e.g., spatiotemporal GCN) are identified as essential directions. Moreover, a conceptual AirTraffic LLM is suggested via a novel Spatial-Temporal Causal Large Language Model (STC-LLM) framework for high-precision flight delay prediction, which requires further experimental validation and real-world testing. Nevertheless, issues such as data privacy, model opacity, and high computational costs must be carefully addressed when applying LLMs. Finally, the findings are expected to enhance understanding of delay propagation among researchers, practitioners, and policymakers, while providing insights and guidance to airports, airlines, and air traffic control.