A Spatiotemporal Neural Network Model for Estimated-Time-of-Arrival Prediction of Flights in a Terminal Maneuvering Area

Affected by the nondeterministic nature of flight trajectories, the external environment, and airport operational conditions, the prediction of the estimated time of arrival (ETA) is one of the most challenging tasks for air traffic control in the terminal maneuvering area (TMA). Previous studies lack adequate utilization of the spatial and temporal behaviors embedded in continuous trajectories. We propose a novel spatiotemporal neural network model for estimating the time of arrival (STNN-ETA), which consists of three components: 1) trajectory pattern recognition, which classifies historical trajectories into several patterns/clusters; 2) trajectory prediction, which predicts a target flight's subsequent positions based on trajectory pattern matching; and 3) arrival time prediction, in which nonlinear function and recurrent units are adopted to capture spatiotemporal features for prediction purposes. In the proposed model, we also utilize spatial and temporal attention mechanisms to focus on important features from radar echo maps and trajectory series, respectively, and suppress unnecessary ones for ETA prediction. To validate the effectiveness of the proposed method, we apply it to predict the ETA of flights within the Beijing TMA. Extensive experiments show that STNN-ETA outperforms the state-of-the-art models in terms of the mean absolute error.