In order to improve the accuracy of flight delay time at airports,the prediction model was investi-gated and a flight delay prediction model based on SSA-CNN-BiLSTM was proposed by using Sparrow Search Algorithm(SSA)in combination with Convolutional Neural Network(CNN)and Bidirectional Long and Short-Term Memory Network(BiLSTM).Validation is carried out using actual operational data from Atlanta Airport in the U.S.A.Comparison tests are conducted with benchmark models such as BiLSTM,CNN-LSTM,and the model performance is verified by adding the dual feature dataset of flow and time.The results show that the SSA-CNN-BiLSTM model performs optimally in the evaluation indexes,with a mean absolute error(MAE)of 5.15 and a root mean square error(RMSE)of 7.58,and the prediction accuracy is better than that of the benchmark model,with good multi-feature processing capability.
flight delay predictionparameter optimizationconvolutional neural networksbidirectional long and short-term memory networkssparrow search algorithm
万方数据
维普
