## 5 Results and Discussion
### Comparison of Model Results
#### 5.3.2 GWR Regression Analysis  
Using MGWR4.0 software, the regression results of GWR are obtained (see Table 6 below). The specific operation is as follows: Due to the faster convergence speed of GWR, this paper chooses GWR estimation as the initial estimation. The bandwidth length is adjusted according to the sample point adaptive kernel bandwidth determination method. The bandwidth selects the golden section mode and the spatial kernel function, and the bandwidth selection criterion selects the quadratic kernel function and the modified Akaike information criterion (AIC\({}_{\text{C}}\)). Since the convergence criterion selects SOC-f to converge more strictly than SOC-RSS, SOC-f convergence is selected. To improve the prediction accuracy of the model, the order of magnitude of the convergence error is set to \(10^{-5}\). When the fluctuation range of the fitting coefficient is less than \(10^{-5}\), the iteration is completed.  
From the analysis of Table 6, it is evident that the bandwidth of the independent variables is small, indicating that the explanatory variables have a significant spatial heterogeneity effect on passenger flow in Shenzhen's rail transit system as a whole, and the regression coefficients of each variable are significantly different. Log-likelihood measures the probability of data occurrence given model parameters. The larger the log-likelihood, the more likely the observed data are to occur under these parameters, meaning the model can better explain the data, resulting in a better fit. The log-likelihood (\(-\)131.691) of the GWR model is higher than that of the OLS model (\(-\)181.871). At the same time, the R\({}^{2}\) (0.714) adjusted R\({}^{2}\) (0.655) value and logarithmic probability of the GWR model are higher than those of the OLS model. The residual sum of squares and AIC\({}_{\text{C}}\) are smaller  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Unstandardized**} & \multicolumn{3}{c}{**Collinearity Statistics**} \\  & \multicolumn{3}{c}{**Coefficients**} & & & \\
**Variable** & **Coefficient** &
\begin{tabular}{c} **Standard** \\ **Error** \\ \end{tabular} & **t** & **P** & **Tolerance** & **VIF** \\ \hline Constant & 0.000 & 0.057 & 4.364 & 0.000 & - & - \\ \(X_{2}\) & \(-\)0.163 & 0.064 & \(-\)2.545 & 0.012 & 0.8 & 1.25 \\ \(X_{6}\) & 0.601 & 0.090 & 6.66 & 0.000 & 0.402 & 2.488 \\ \(X_{7}\) & \(-\)0.183 & 0.085 & \(-\)2.148 & 0.033 & 0.449 & 2.226 \\ \(X_{9}\) & 0.278 & 0.065 & 4.297 & 0.000 & 0.78 & 1.281 \\ \(X_{13}\) & \(-\)0.229 & 0.065 & \(-\)3.522 & 0.001 & 0.777 & 1.286 \\ Residual sum of squares & & & & 86.949 & & \\ Log-likelihood & & & \(-\)181.871 & & & \\ R\({}^{2}\) & & & & 0.476 & & \\ Adjusted R\({}^{2}\) & & & & 0.460 & & \\ AIC\({}_{\text{C}}\) & & & & 378.451 & & \\ \hline \hline \end{tabular} Note: Sig. = significance level of a variable. If sig is \(<\)0.05, it is significant.  
\end{table}
Table 5: Regression results of the OLS model.  
than those of the OLS model, indicating that the GWR model has a better fitting ability for describing the peak passenger flow of Shenzhen's rail transit system.