Multi-Attribute Data-Driven Flight Departure Delay Prediction for Airport System Using Deep Learning Method

Abstract

Complex and diverse multi-attribute flight data can provide data-driven opportunities for airport flight delay prediction. However, it is a challenge to effectively and efficiently process multi-attribute flight data. This paper proposes a hybrid dynamic spatial-temporal long short-term memory network (LSTM) with 3D-directional multi-attribute features (3DF-DSCL) for departure flight delay prediction. The model is based on a 3D convolutional neural network (3D-CNN), graph convolutional network (GCN) and long short-term memory networks (LSTM) model. Firstly, the dataset divides the state and environment of departure flight delay into three situations, including the dynamic operation link, which integrates the trajectory system of aircraft movement in the terminal area, the network congestion link caused by aircraft multi-area movement in the air and ground, and other delay factors determined by the airport take-off and landing requirements. Multi-attribute data are divided into time series, spatial-temporal network and dynamic moving trajectory grid input variables. Among them, the spatial network and dynamic moving trajectory grid data are the inputs of GCN and 3D CNN models, which aim to extract spatial-temporal features. The time series input variables are fed into LSTM. These features are then integrated and fed into LSTM for flight delay prediction, where the flight delay of airport outbound flights is taken as the output variable. The case study shows that the proposed method can significantly improve the accuracy of flight prediction delay. The Mean Absolute Error (MAE) can reach 0.26, which is a 14.47% reduction compared with 2D CNN+GCN+LSTM.