Resumen
Accurate flight departure time prediction enables the rational use of airport support resources, aprons, and runway resources, and promotes the implementation of collaborative decision-making. In order to accurately predict the flight departure time, this paper proposes a deep learning-based flight departure time prediction model. First, this paper analyzes the influence of different factors on flight departure time and the influencing factor. Secondly, this paper establishes a gated recurrent unit (GRU) model, considers the impact of different hyperparameters on network performance, and determines the optimal hyperparameter combination through parameter tuning. Finally, the model verification and comparative analysis are carried out using the real flight data of ZSNJ. The evaluation values of the established model are as follows: root mean square error (RMSE) value is 0.42, mean absolute percentage error (MAPE) value is 6.07, and mean absolute error (MAE) value is 0.3. Compared with other delay prediction models, the model established in this paper has a 16% reduction in RMSE, 34% reduction in MAPE, and 86% reduction in MAE. The model has high prediction accuracy, which can provide a reliable basis for the implementation of airport scheduling and collaborative decision-making.