Abstract: To address the problem of how air traffic flow management departments can implement flow management more ef鄄
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.

Key words: air traffic flow management, air traffic network flow system (ATNFS), hidden Markov model (HMM), grey wolf
optimization (GWO) algorithm, situation awareness, situation prediction