Aiming at the fact that most of the existing security inspection passenger flow prediction studies are in normal conditions,without considering the change trend of security inspection passenger flow in abnormal emergencies,a Gaussian mixture model(GMM)clustering based AM-BiLSTM airport security inspection passenger flow prediction model is proposed.Firstly,GMM clustering algorithm is used for clustering and analysis on the usage date characteristics and delay characteristics of the original dataset,and different AM-BiLSTM passenger flow prediction models are constructed according to the different daily security inspection passenger flow scenarios obtained by clustering.The experimental results show that,compared with the existing several prediction methods,this method can accurately predict the security check passenger flow in different scenarios.
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