Flight delays due to holding maneuvers are a critical and costly phenomenon in aviation, driven by the need to manage air traffic congestion and ensure safety. Holding maneuvers occur when aircraft are instructed to circle in designated airspace, often due to factors such as airport congestion, adverse weather, or air traffic control restrictions. This study models the prediction of flight delays due to holding maneuvers as a graph problem, leveraging advanced Graph Machine Learning (Graph ML) techniques to capture complex interdependencies in air traffic networks. Holding maneuvers, while crucial for safety, cause increased fuel usage, emissions, and passenger dissatisfaction, making accurate prediction essential for operational efficiency. Traditional machine learning models, typically using tabular data, often overlook spatial-temporal relations within air traffic data. To address this, we model the problem of predicting holding as edge feature prediction in a directed (multi)graph where we apply both CatBoost, enriched with graph features capturing network centrality and connectivity, and Graph Attention Networks (GATs), which excel in relational data contexts. Our results indicate that CatBoost outperforms GAT in this imbalanced dataset, effectively predicting holding events and offering interpretability through graph-based feature importance. Additionally, we discuss the model's potential operational impact through a web-based tool that allows users to simulate real-time delay predictions. This research underscores the viability of graph-based approaches for predictive analysis in aviation, with implications for enhancing fuel efficiency, reducing delays, and improving passenger experience.

翻译：由于盘旋机动导致的航班延误是航空领域一个关键且代价高昂的现象，其根源在于需要管理空中交通拥堵并确保飞行安全。当飞机因机场拥堵、恶劣天气或空中交通管制限制等因素被指令在指定空域盘旋时，即发生盘旋机动。本研究将因盘旋机动导致的航班延误预测建模为一个图问题，利用先进的图机器学习技术来捕捉空中交通网络中复杂的相互依赖关系。盘旋机动虽对安全至关重要，但会导致燃油消耗增加、排放增多以及乘客满意度下降，因此对其进行准确预测对于提升运行效率至关重要。传统的机器学习模型通常使用表格数据，往往忽略了空中交通数据中的时空关联。为解决这一问题，我们将盘旋预测问题建模为有向（多）图中的边特征预测任务，在此框架下同时应用了两种方法：一是通过融入捕捉网络中心性与连通性的图特征进行增强的CatBoost模型；二是擅长处理关系数据的图注意力网络。我们的结果表明，在此类不平衡数据集中，CatBoost的表现优于图注意力网络，能够有效预测盘旋事件，并通过基于图的特征重要性分析提供可解释性。此外，我们通过一个基于网络的工具探讨了该模型的潜在运行影响，该工具允许用户模拟实时延误预测。本研究论证了基于图的方法在航空预测分析中的可行性，对提升燃油效率、减少延误及改善乘客体验具有重要启示。