用排队理论+注意力机制预测航班延误，跨网络表现优异
Queue up for takeoff: a transferable deep learning framework for flight delay prediction
- 结合排队理论与简单注意力机制建模延误传播
- 美国数据上准确率0.927，F1达0.932，欧洲数据仍保持高精度
- 适合航空公司和机场做实时延误预警与调度优化
航班延误是航空业的重大挑战，造成显著的财务和运营损失。为提升乘客体验并减少收入损失，航班延误预测模型需具备高精度与跨网络泛化能力。本文提出一种融合排队理论与简易注意力机制的新方法，称为队列理论SimAM（QT-SimAM）。基于美国交通统计局数据验证，所提双向QT-SimAM模型在准确率0.927、F1分数0.932上优于现有方法。为评估可迁移性，模型在EUROCONTROL数据集上测试，仍取得0.826准确率与0.791 F1分数。本研究构建了一套高效端到端的航班延误预测方法，其在不同航网中均能高精度预测延误，有助于缓解乘客焦虑并支持运营决策。
原文摘要 · Abstract (English)
Flight delays are a significant challenge in the aviation industry, causing major financial and operational disruptions. To improve passenger experience and reduce revenue loss, flight delay prediction models must be both precise and generalizable across different networks. This paper introduces a novel approach that combines Queue-Theory with a simple attention model, referred to as the Queue-Theory SimAM (QT-SimAM). To validate our model, we used data from the US Bureau of Transportation Statistics, where our proposed QT-SimAM (Bidirectional) model outperformed existing methods with an accuracy of 0.927 and an F1 score of 0.932. To assess transferability, we tested the model on the EUROCONTROL dataset. The results demonstrated strong performance, achieving an accuracy of 0.826 and an F1 score of 0.791. Ultimately, this paper outlines an effective, end-to-end methodology for predicting flight delays. The proposed model's ability to forecast delays with high accuracy across different networks can help reduce passenger anxiety and improve operational decision-making
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