  Airport performance prediction with a reasonable look-ahead time is a
challenging task and has been attempted by various prior research. Traffic,
demand, weather, and traffic management actions are all critical inputs to any
prediction model. In this paper, a novel approach based on Temporal Fusion
Transformer (TFT) was proposed to predict departure and arrival delays
simultaneously for multiple airports at once. This approach can capture complex
temporal dynamics of the inputs known at the time of prediction and then
forecast selected delay metrics up to four hours into the future. When dealing
with weather inputs, a self-supervised learning (SSL) model was developed to
encode high-dimensional weather data into a much lower-dimensional
representation to make the training of TFT more efficiently and effectively.
The initial results show that the TFT-based delay prediction model achieves
satisfactory performance measured by smaller prediction errors on a testing
dataset. In addition, the interpretability analysis of the model outputs
identifies the important input factors for delay prediction. The proposed
approach is expected to help air traffic managers or decision makers gain
insights about traffic management actions on delay mitigation and once
operationalized, provide enough lead time to plan for predicted performance
degradation.
