## 4 Experimental Results and Analysis
### Model Settings  
This paper compares the proposed FINM with the most common passenger flow prediction models in recent years, CNNs, CNN-Attention, CNN-LSTM, LSTMs, LSTM-Attention, BiLSTMs, BiLSTM-Attention, GRUs, and CNN-BiLSTM-GRU-Attention, by consulting industry experts and the research literature. The main factors affecting airport express passenger flow include the historical data for the express passenger flow, the passenger flow of the transfer flight, the weather, the time period, whether it is a working day, whether it is a holiday, and whether it is a peak period. In order to clarify the key influencing factors in airport express passenger flow from an urban area in the airport direction, based on 14,562 samples, this paper uses the Pearson correlation coefficient and SNR signal-to-noise ratio to preliminarily test the correlation degree between each passenger flow influencing factor and passenger flow from the urban area in the airport direction and selects the characteristics with a large correlation degree as the model input. Specifically, the characteristics include the historical passenger flow in the direction from the urban area to the airport, the weather (the signal-to-noise ratio between sunny, rainy, and snowy weather and express passenger flow is 0.236), the time period (the signal-to-noise ratio between the time period and express passenger flow is 0.643), whether it is a working day (the signal-to-noise ratio between a working day and the express passenger flow is 0.356), whether it is a holiday (the signal-to-noise ratio between a holiday and the express passenger flow is 0.251), and whether it is a peak hour (the signal-to-noise ratio between a peak hour and the express passenger flow is 0.312). Each model uses the feature vectors of the first six time slices of the target prediction time slice to predict the passenger flow of the target prediction time slice.  
Parameter Settings:  
CNN, CNN-Attention, CNN-LSTM: input channel = 1, output channel = 64, convolution kernel = 2, pooling kernel = 2, batch _ size = 32, epochs = 50.  
LSTM, LSTM-Attention, BiLSTM, BiLSTM-Attention: batch _ size = 32, timesteps = 1, epochs = 50, LSTM unit number = 64, hidden layer = 1, loss function MSE, activation function Sigmoid, optimization function Adam.  
GRU and CNN-BiLSTM-GRU-Attention: batch_size = 32, epochs = 50, loss function MSE, activation function Sigmoid, optimization function Adam.  
FINM: input channel = 1, output channel = 64, convolution kernel = 2, pooling kernel = 2, GRU layer input feature number = 64, hidden state feature number = 32, loss function MSE, activation function Sigmoid, optimization function Adam.  
The following Algorithm 1 Training Process for FINM describes the training process for the FINM. Firstly, the training set data and the initial model parameters are input, and the number of training rounds is set. For each round of training, the training set data are first obtained directly, and then the data are transformed through a series of carefully designed processing steps. Specifically, the cnn_process function is first called to perform CNN-related processing on the data, including convolution, activation, and pooling operations. This step can effectively extract the local features from the data. Then, the shape-adjusted data are entered into the bilstm_process function for BiLSTM processing. The BiLSTM can better capture the long-term dependence of sequence data by virtue of its bidirectional structure. Then, the data are processed by the GRU through the gru_process function to further mine the information in the data. Finally, the fc_process function processed by the fully connected layer maps the data to the dimension suitable for output so as to obtain the prediction results.  
```
1:Input: Training set \(X_{train}\); Initial HybridModel parameters \(\theta_{0}\); Training epoch;
2:Output: The optimized HybridModel parameters \(\theta_{epoch}\);
3:for \(i=1\) to epoch do
4:\(x\gets X_{train}\);
```  
**Algorithm 1 Training Process of FINM**