Aircraft Trajectory Prediction Based on Bayesian Optimised Temporal Convolutional Network-Bidirectional Gated Recurrent Unit Hybrid Neural Network

Abstract

Efficient and accurate flight trajectory prediction is a key technology for promoting intelligent and informative air traffic management and improving the operational capabilities and predictability of air traffic. To address the problems in extracting hidden information from historical trajectory information, the approach must accurately select high-dimensional features related to the prediction target and overcome the short-term memory of the time series. Herein, we present a novel trajectory prediction model based on a dual-self-attentive (DSA)-temporal convolutional network (TCN)-bidirectional gated recurrent unit (BiGRU) neural network. In this model, the TCN provides highly stable training, high parallelism, and a flexible perceptual domain. The self-attentive mechanism of the TCN structure can focus on features that contribute the most to the output. After the TCN, the BiGRU network combined with the self-attentive mechanism is used to further bidirectionally mine the connections between the features and outputs of the trajectory sequence, and a Bayesian algorithm is used to optimise the hyperparameters of the model for optimal performance. A comparison and validation based on current well-known neural network models (i.e., CNN, TCN, GRU, and their variants) shows that the DSA-TCN-BiGRU model based on Bayesian hyperparameter optimisation has the best performance. Therefore, the improved predictive model is applicable and valuable, providing a basis for future decision trajectory-based operations.