## 5 Results and Discussion
### Comparison of Model Results
#### 5.3.4 Comparative Analysis of Prediction Accuracy of OLS and GWR Model  
In order to further verify the accuracy of the GWR model in predicting the passenger flow of rail transit, this paper uses the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) as the evaluation indexes and compares the prediction and simulation accuracy of the GWR model with the OLS model, as shown in Table 8 below. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Regression Coefficient**} \\ \hline
**Variable** & **Bandwidth** & **Minimum** & **Maximum** & **Mean** & **Median** & **STD** \\ \hline Constant & 71.000 & \(-\)2.221 & 0.689 & \(-\)0.317 & \(-\)0.057 & 0.734 \\ \(X_{2}\) & 71.000 & \(-\)0.314 & \(-\)0.028 & \(-\)0.150 & \(-\)0.154 & 0.073 \\ \(X_{6}\) & 71.000 & \(-\)0.209 & 1.257 & 0.482 & 0.603 & 0.315 \\ \(X_{7}\) & 71.000 & \(-\)0.363 & 0.987 & 0.173 & 0.215 & 0.436 \\ \(X_{9}\) & 71.000 & \(-\)0.121 & 0.597 & 0.334 & 0.337 & 0.170 \\ \(X_{13}\) & 71.000 & \(-\)3.548 & 0.477 & \(-\)0.799 & \(-\)0.371 & 1.002 \\ Residual sum of squares & & & & 47.501 & & \\ Log-likelihood & & & \(-\)131.691 & & & \\ R2 & & & 0.714 & & & \\ Adjusted R2 & & & 0.655 & & & \\ AICc & & & 334.509 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Regression results of the GWR model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{5}{c}{**Regression Coefficient**} \\ \hline  & **OLS Without** & **OLS With** & **GWR Without** & **GWR With** \\  & \(\mathbf{X_{2}}\) & \(\mathbf{X_{2}}\) & \(\mathbf{X_{2}}\) & \(\mathbf{X_{2}}\) \\ \hline
**Residual sum of** & & & & \\
**squares** & & & & \\
**Log-likelihood** & \(-\)185.192 & \(-\)181.871 & \(-\)135.807 & \(-\)131.691 \\ R2 & 0.463 & 0.476 & 0.699 & 0.714 \\
**Adjusted R2** & 0.455 & 0.460 & 0.640 & 0.655 \\
**AICc** & 380.912 & 378.451 & 339.701 & 334.509 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of regression results of models considering average house price. The lower accuracy of passenger flow prediction for commercial housing stations indicates that Shenzhen, as a special economic zone and an immigrant city [50], has high population mobility. The travel behavior of residents is affected by multiple factors, including the frequency of commercial activities, diverse transportation options, and the fast pace of urban life. This complex travel behavior increases the difficulty of prediction, resulting in the relatively low accuracy of passenger flow forecasts for residential stations. Passenger flow predictions for transportation hub stations show instability. Transportation hubs in the city center have higher prediction accuracy, whereas those further from the center have lower accuracy. This indicates that transportation hubs in central Shenzhen are surrounded by well-developed business districts, where passenger flow tends to be relatively concentrated and stable. Areas farther away from the city center are not as developed as the city center, and travel choices are more homogeneous with uncertainties. The higher accuracy and stability of passenger flow forecasts for servicing business and scenic spot stations indicate that the built environment around commercial stations in Shenzhen is strongly developed, attracting a large number of consumers and tourists. Traffic in these areas is more stable and predictable, as commercial activities tend to have a fixed regularity (e.g., commuting peaks, holiday shopping, etc. ), which is consistent with the urban nature of Shenzhen as a national economic center city. Although there are natural landscapes in the scenic spot stations, the surrounding built environment has formed a more stable tourism economic circle due to the influence of urban special economic zones, and the passenger flow prediction model has a better fitting effect.