A Method for Predicting Air Traffic Flow Based on a Combined GA, RBF, and Improved Cao Method

Considering the chaotic characteristic of air traffic flow time series data, a prediction model based on the phase space reconstruction theory is proposed to improve the accuracy and effectiveness of previous air traffic flow prediction methods, which combines genetic algorithm (GA), radial basis function (RBF) neural network (NN) and improved Cao method. First, to reduce the error introduced by the human in the traditional Cao method and improve the accuracy of phase space reconstruction, the criteria for determining the dimension of the reconstructed phase space is developed by identifying false neighboring points and iteratively comparing the deviation of the embedded dimension with its acceptable limits. In this way, reconstructed air traffic flow time series data is developed. Secondly, to improve the prediction accuracy of the traditional RBF neural network, GA is employed to optimize center vectors, weight coefficients, and output layer thresholds of the neural network. Then, the reconstructed time series are predicted by the calibrated RBF neural network with optimal coefficients. Finally, the proposed method is verified using the observed air traffic flow data, the effectiveness of the prediction is evaluated, and the influence of the time scale on the accuracy is analyzed by incorporating the maximal Lyapunov exponent and the quality of the prediction. Study results show that ①the proposed method fits the nonlinear data well and improves the accuracy of traffic flow prediction. ②Taking the prediction with a 5-min time interval as the instance, compared with the traditional RBF neural network, the mean absolute errors (MAE), mean square errors (MSE) and mean absolute percentage error (MAPE) is reduced by 19.44%, 34.78%, and 27.21%, respectively. ③Compared with the back propagation (BP) neural network and the long short-term memory (LSTM) neural network model, the MAE of the proposed method is reduced by 36.20% and 16.10%, respectively, and the response speed is increased by 27.42% and 35.00%. In summary, the proposed method can explain the intricate chaotic properties of the system and improves the accuracy and efficiency of air traffic flow prediction.