Abstract:

To address the challenges of severe data missingness， high uncertainty in influencing factors， and limited prediction accuracy in flight ground service milestone event prediction， a flight ground service milestone event prediction framework named CTrans-ATGCN was proposed， including two core components： missing data imputation and milestone event prediction. First， based on the Transformer architecture， a convolutional layer was introduced to extract the temporal and topological features in flight ground service milestone event data， and a self-attention mechanism was employed to model potential global dependencies among different milestone events， thereby constructing CTrans （Convolutional Transformer）， a method for flight ground service data imputation that integrates convolutional self-attention mechanism. On this basis， a milestone event prediction method， Attention-based Temporal Graph Convolutional Network （ATGCN）， was constructed. In this method， a Temporal Graph Convolutional Network （T-GCN） was utilized to capture spatio-temporal dependencies in historical milestone event data， and by combining an attention mechanism with a scoring function， the weights among different milestone event nodes were adjusted dynamically， so as to achieve high-precision prediction for ground service milestone events. Experimental results on a 2023 real-world dataset from an airport in North China show that in a non-random missing scenario with a 70% missing rate， the proposed imputation method reduces the Mean Absolute Error （MAE） by approximately 14.91% compared to the suboptimal model GraphSAGE （Graph Sample and AggreGatE）； on low-missing-rate datasets， the proposed “imputation + prediction” method achieves prediction performance similar to that of Sliding Bi-Directional Graph Convolutional Neural network Model （SBDGCNM）； on datasets with 70% missing rate， CTrans-ATGCN achieves the MAE and Root Mean Square Error （RMSE） of 14.76 and 18.42， respectively， outperforming all baseline methods. It can be seen that this framework provides an effective method for flight ground service prediction under missing data， and offers reliable support for efficient scheduling of ground personnel and equipment.

Key words: flight ground service, Graph Convolutional Network (GCN), time series imputation, milestone event prediction, Transformer model, attention mechanism

摘要：

针对航班地面保障里程碑事件预测面临的数据缺失严重、影响因素不确定和预测精度不高等问题，提出一种航班地面保障里程碑事件预测框架CTrans-ATGCN，包括缺失数据填补和里程碑事件预测两大核心组件。该框架基于Transformer架构，引入卷积层提取航班地面保障里程碑事件数据中蕴含的时序和拓扑特征，采用自注意力机制建模不同里程碑事件之间潜在的全局依赖关系，构建融合卷积自注意力机制的航班地面保障数据填补方法CTrans（Convolutional Transformer）。在此基础上，构建里程碑事件预测方法——基于注意力的时序图卷积网络（ATGCN）。该方法利用时序图卷积网络（T-GCN）捕捉里程碑事件的历史数据中蕴含的时空依赖关系，并结合注意力机制与评分函数动态地调整不同里程碑事件节点之间的权重，从而实现对地面保障里程碑事件的高精度预测。在华北地区某机场2023年真实数据集上的实验结果表明，所提填补方法在缺失率为70%的非随机缺失场景下，相较于次优模型GraphSAGE（Graph Sample and AggreGatE）的平均绝对误差（MAE）降低了约14.91%；而在低缺失率数据集上，所提“填补+预测”方法取得了与滑动双向图卷积神经网络模型（SBDGCNM）相当的预测效果；在缺失率为70%的数据集上，CTrans-ATGCN的MAE和均方根误差（RMSE）分别为14.76和18.42，优于所有基准方法。可见，该框架为解决数据缺失下的航班地面保障预测问题提供了有效方法，为地勤高效调度人员与设备提供了可靠支持。

关键词: 航班地面保障, 图卷积网络, 时间序列填补, 里程碑事件预测, Transformer模型, 注意力机制