Queue up for takeoff: a transferable deep learning framework for flight delay prediction

TL;DR

This paper presents QT-SimAM, a novel deep learning framework combining Queue-Theory and attention mechanisms, achieving high accuracy in flight delay prediction and demonstrating strong transferability across different datasets.

Contribution

Introduces QT-SimAM, a transferable deep learning model that integrates Queue-Theory with attention mechanisms for accurate flight delay prediction across networks.

Findings

Achieved 0.927 accuracy and 0.932 F1 score on US data.

Demonstrated transferability with 0.826 accuracy on EUROCONTROL data.

Outperformed existing methods in flight delay prediction.

Abstract

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