Fast response flight multi-service nodes prediction based on one hidden layer neural network

Abstract

Airport scene multi-service process(ASMP) state prediction is one of the important links to optimize airport ground resource allocation, improve collaborative release scheduling, and improve airport operation efficiency. In order to solve the problem of the slow response of existing prediction models, a Gaussian kernel probability density model of multi-service nodes in airport scenes is established. Then the one hidden layer neural network(OHLNN) is used to predict the nodes of ASMP. A study on the data of more than 20,000 flights at a large airport in China shows that: The mean absolute error (MAE) of the mixed flight prediction result is only 2.62911min, the mean root mean square error (RMSE) is only 3.24564min, and the response speed of the prediction model in the verification set is about 0.48s. When the results of MAE and RMSE are good, the response speed of the prediction model is greatly improved.