## 4 Experimental Results and Analysis
### Experimental Designs  
We selected a total of 396 days of data in a specific time period as the research basis. We extracted 20% of the 396-day data as a test set. After calculation, the test set contained 2930 samples. The remaining 80% of the day's data were used as the training set, which contains 11,722 samples. Based on the above data division, we set up four sets of experiments. The purpose of setting up multiple sets of experiments was to verify the effectiveness, stability, and generalization ability of the proposed method or model from different angles and levels. Each group of experiments was carried out independently 10 times during the specific development process. Through multiple independent experiments, the influence of random errors that may occur in a single experiment on the results can be effectively reduced, so that the final experimental results are more reliable and more convincing. After completing the 10 independent experiments in each group, for the 10 results obtained by each group of experiments, we calculated the average value, standard deviation, and other statistics for the key indicators in the 10 results. By calculating the average value, a stable and comprehensive performance in the group of experiments for the key indicators could be ensured. The standard deviation could reflect the degree of dispersion in the 10 results for this key indicator, which helped us to understand the fluctuation in the experimental results. Finally, the results of the four groups of experiments after the above aggregation methods were comprehensively compared and analyzed so as to comprehensively evaluate the performance of the proposed method or model under different conditions and provide solid data support for the subsequent research conclusions. We used Loss Value, MAE, MSE, RMSE, MAPE, and R-Squared to evaluate the performance of the airport express passenger flow prediction model based on the FINM. These indicators reflect the degree of deviation and goodness of fit between the model prediction results and the real values from different perspectives, which is of great significance for a comprehensive and in-depth understanding of the performance of the model. MAE is mainly used to measure the average absolute error between the predicted value and the real value of passenger flow distribution in the train section. It intuitively reflects the degree to which the predicted value deviates from the true value in the average sense. \[MAE=\frac{1}{n}{\sum_{i=1}^{n}}{|y_{i}-\hat{y}_{i}|} \tag{17}\]  
Among them, \(y_{i}\) represents the real observation value of the \(i\) sample, \(\hat{y}_{i}\) represents the model prediction value of the \(i\) sample, and \(n\) is the number of samples. The smaller the value of MAE, the smaller the average deviation between the predicted value and the real value of the model, and the higher the prediction accuracy of the model. MSE measures the average of the squared error between the predicted value and the true value. \[MSE=\frac{1}{n}{\sum_{i=1}^{n}}(y_{i}-\hat{y}_{i})^{2} \tag{18}\]  
MSE squares the error, which means that a larger error is given a greater weight in the calculation. Compared with MAE, MSE is more focused on penalizing larger errors. Inpractical applications, if the model produces individual large prediction errors, the value of MSE will increase significantly, which reflects the poor performance of the model for these outliers. RMSE is used to measure the sample standard deviation between the predicted value and the true value of the passenger flow distribution in the train section. \[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})}^{2}} \tag{19}\]  
RMSE is similar to MSE, but because of the square root operation, it restores the dimension of the error to being the same as the original data, making it easier to understand in a practical sense. For example, if the passenger flow data are based on the number of people, then the value of the RMSE is the size of the average prediction error per sample (in terms of the number of people).