## 5 Experimental Results
### Analysis of Prediction Results  
Figure 3 illustrates the distribution of predicted values versus actual values when the model comprehensively considered factors such as temperature, precipitation, and the actual metro flow of the two stations that were most correlated to the target station based on the transformer method. According to the figure, it is evident that the distribution of predicted values closely aligns with the actual values, with most predictions closely \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Time Interval**} & \multicolumn{6}{c}{**Evaluating Indicators**} \\ \cline{2-13}  & \multicolumn{2}{c}{**Zhonglou Station**} & \multicolumn{2}{c}{**Zhonglou Station (Exit)**} & \multicolumn{2}{c}{**Hangtiancheng Station (Entry)**} & \multicolumn{2}{c}{**Hangtiancheng Station (Exit)**} & \multicolumn{2}{c}{**Hangtiancheng Station (Exit)**} \\ \cline{2-13}  & **RMSE** & **MAE** & **MAPE** & **RMSE** & **MAE** & **MAPE** & **RMSE** & **MAE** & **MAPE** & **RMSE** & **MAE** & **MAPE** \\ \hline
24 & 75.25 & 49.32 & 13.69\% & 91.08 & 61.42 & 22.71\% & 56.31 & 36.30 & 25.37\% & 61.15 & 40.46 & 39.58\% \\ \hline
28 & 78.20 & 51.76 & 13.67\% & 91.99 & 59.51 & 23.55\% & 56.10 & 34.75 & 16.37\% & 60.59 & 41.24 & 44.43\% \\ \hline
32 & 74.90 & 48.54 & 20.52\% & 88.77 & 57.41 & 20.76\% & 56.12 & 34.04 & 16.54\% & 58.67 & 39.05 & 42.97\% \\ \hline
36 & 78.73 & 50.21 & 17.14\% & 88.67 & 60.03 & 14.85\% & 56.15 & 34.82 & 18.25\% & 59.82 & 39.55 & 35.64\% \\ \hline
40 & 74.51 & 48.82 & 17.14\% & 90.41 & 63.14 & 35.93\% & 55.98 & 37.83 & 31.40\% & 54.39 & 37.23 & 31.49\% \\ \hline
44 & 73.10 & 49.22 & 19.02\% & 88.45 & 60.95 & 20.05\% & 54.29 & 33.46 & 20.82\% & 54.62 & 36.97 & 36.77\% \\ \hline
48 & 71.27 & 46.85 & 10.35\% & 85.78 & 59.50 & 26.49\% & 53.46 & 33.51 & 19.12\% & 52.80 & 35.19 & 18.78\% \\ \hline
52 & 69.27 & 45.34 & 16.02\% & 87.24 & 59.57 & 15.40\% & 53.83 & 32.35 & 17.89\% & 54.01 & 36.04 & 30.34\% \\ \hline
56 & 70.55 & 47.89 & 19.14\% & 86.28 & 55.52 & 13.97\% & 55.01 & 33.05 & 17.83\% & 53.69 & 36.27 & 27.52\% \\ \hline
60 & 67.63 & 45.30 & 14.75\% & 93.86 & 60.84 & 18.00\% & 54.74 & 33.40 & 19.63\% & 53.60 & 36.65 & 35.14\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The entry and exit flow prediction results of different historical time intervals. matching the real data. The model successfully captured the peak and off-peak patterns of entry and exit flow. In Figure 3a, the peaks and troughs are marked with hollow circles. The peak occurred during the evening rush hours, specifically between 17:00 and 19:00, while the trough corresponds to the early morning hours when the subway services are temporarily halted. Furthermore, the experimental results indicate that the peak entry and exit flows at Zhonglou Station are generally higher than those at Hangtiancheng Station. Both stations exhibited certain patterns, with variations in entry and exit flow between weekdays and weekends. Specifically, the peak entry and exit flows at Zhonglou Station typically ranged between 2000 and 2500, whereas at Hangtiancheng Station, the peak flow usually ranged between 1400 and 2000. This study then explained the flow changes from the perspective of urban functional zones. POI data within an 800 m radius around Zhonglou Station and Hangtiancheng Station were collected, as shown in Table 4. The data in the table refers to the number of POIs of a specific type within 800 m of the site. For example, 884 refers to the number of food and beverage services within 800 m of Zhonglou Station. The results indicate significantly higher numbers for sports and leisure services, accommodation services, public facilities, and scenic spots around Zhonglou Station (256, 900, 91, 35, respectively) compared to Hangtiancheng Station (53, 166, 10, 7, respectively). This suggests that Zhonglou Station serves as a commercial and tourist hub with a relatively high population density.