Simulation of Air Traffic Flow Prediction in Terminal Area Considering Fluctuation Factor
Traditional parameter regression algorithm cannot effectively and accurately predict the time series data of air traffic flow in the short term.In order to improve the ability of airspace traffic management and the accuracy and stability of short-term flow prediction,this paper organically integrated the CV data period-based prediction mecha-nism into the Long-Short-Term Memory Recurrent Network(LSTM).The short-term prediction model of CV-LSTM air traffic flow time series data was constructed.Firstly,the HBD data were decomposed into low frequency compo-nents and noise components by using the WT algorithm,and then the discrete data cycle analysis was used to denoise the high frequency noise components by analyzing the periodicity and spatio-temporal similarity of the data;Then,based on the characteristics of the data,the basic prediction model of CV cycle was constructed to predict and elimi-nate the CVlatility of the data.Finally,by constructing the LSTM flow prediction model,the air traffic flow prediction task was completed in the secondary optimization of BP network.The simulation results show that the CV-LSTM mod-el has the best tracking performance in the 10 min interval time series of HBD data set,and compared with the other five baseline models,the P,R and F1 indicators are improved by 14.76%,15.55%and 13.57%,respectively.To sum up,the CV-LSTM air traffic flow time series short-term prediction model has the highest prediction accuracy and high prediction stability,and has important research value in air traffic flow prediction simulation.
万方数据
维普
