Airport Delay Prediction with Temporal Fusion Transformers

TL;DR

This paper introduces a novel application of the Temporal Fusion Transformer model to predict numerical airport delays at a fine-grained level, incorporating diverse operational and weather data for improved accuracy and interpretability.

Contribution

The study applies the Temporal Fusion Transformer to airport delay prediction, providing a more detailed and interpretable model compared to previous categorical and aggregated approaches.

Findings

Model achieves small prediction errors on test data

Incorporates diverse data sources including weather and traffic

Provides insights into key delay factors

Abstract

Since flight delay hurts passengers, airlines, and airports, its prediction becomes crucial for the decision-making of all stakeholders in the aviation industry and thus has been attempted by various previous research. However, previous delay predictions are often categorical and at a highly aggregated level. To improve that, this study proposes to apply the novel Temporal Fusion Transformer model and predict numerical airport arrival delays at quarter hour level for U.S. top 30 airports. Inputs to our model include airport demand and capacity forecasts, historic airport operation efficiency information, airport wind and visibility conditions, as well as enroute weather and traffic conditions. The results show that our model achieves satisfactory performance measured by small prediction errors on the test set. In addition, the interpretability analysis of the model outputs identifies the important input factors for delay prediction.