To address the problem of how air traffic flow management departments can implement flow management more ef-ficiently,the situational awareness theory was applied to the air traffic network flow system(ATNFS)in this paper,and the operational situation prediction model of the air traffic network flow system was established.Firstly,the situational awareness process of air traffic network flow system was provided,and five situation elements including route saturation,irregular flight rate,node saturation,node delayed sortie ratio and node flight cancellation rate,were selected from the perspective of nodes and routes,and the situation values were used as indicators of situation understanding.Secondly,the advantages and disadvantages of hidden Markov model(HMM)were analyzed,and a situation prediction model based on grey wolf optimization(GWO)algorithm and improved HMM was established.Finally,the actual operation data of an air traffic network flow system were used to verify the algorithm.The results showed that the improved prediction model had higher accuracy and more accurate prediction results compared with the original HMM.
air traffic flow managementair traffic network flow system(ATNFS)hidden Markov model(HMM)grey wolf optimization(GWO)algorithmsituation awarenesssituation prediction
维普
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