## 5 Results and Analysis
### Analysis of the Relative Importance of Factors Affecting the Built Environment  
This study employs the SHAP method to investigate the nonlinear impact of built environment factors on rail transit ridership. To further explore the influence of morning peak ridership on built environment factors, we analyse the distribution of SHAP values. Figure 6 displays the distribution of SHAP values for different influencing factors. The horizontal axis represents the positive and negative SHAP values, which explain the positive and negative feedback effects of different factors on total ridership. On the vertical axis, the sum of the absolute SHAP values decreases gradually from top to bottom, indicating a decrease in the relative importance of the influencing factors. Each coloured dot represents an independent sample, and darker colours indicate higher feature values of the built environment factors. The relative importance of these factors is illustrated in Figure 6. As can be seen in Figure 6, the six predictors stand out as having significant relative importance, ranked from highest to lowest as follows: office types, accessibility, road network density, floor area ratio (FAR), public services, and residential types.  
As shown in Figure 7, regarding the impact of office types, rail transit stations surrounded by a high density of office-related built environments exhibit positive SHAP values, indicating that these stations tend to have higher total ridership. This aligns with Shenzhen's status as a special economic zone, where office-related built environments are abundant.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Evaluation Index**} & \multicolumn{3}{c}{**Inbound Station**} & \multicolumn{3}{c}{**Outbound Station**} \\  & **OLS** & **GWR** & **MGWR** & **GBDT** & **OLS** & **GWR** & **MGWR** & **GBDT** \\ \hline R\({}^{2}\) & 0.424 & 0.782 & 0.792 & 0.877 & 0.599 & 0.731 & 0.765 & 0.908 \\ Adjusted R\({}^{2}\) & 0.395 & 0.717 & 0.737 & 0.865 & 0.579 & 0.665 & 0.714 & 0.884 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of the results of the four models: OLS, GWR, MGWR and GBDT.  
Figure 6: Analysis of the relative importance of built environment influences on total ridership.  
Figure 7: Distribution of SHAP values of built environment influences on total ridership.  
For accessibility, SHAP values are predominantly positive in the lower range of Figure 7 and predominantly negative in the higher range. This suggests that stations with better accessibility attract larger ridership.  
From Figure 7, it can also be seen that rail transit stations with higher road network density, floor area ratio (FAR), public services, and residential types primarily show positive SHAP values. Road network density, to some extent, reflects the road capacity, which positively influences total ridership at rail transit stations. This suggests a synergistic relationship between rail transit and public buses. The floor area ratio (FAR), an important indicator of land development intensity, indicates that greater land development intensity leads to higher total ridership on rail transit. Additionally, areas with dense public services and residential built environments tend to have higher ridership at rail stations.  
Other built environment factors have relatively weaker impacts on total rail transit ridership. Therefore, six factors--office types, accessibility, road network density, floor area ratio, public services, and residential types--are selected to analyse the nonlinear relationship with inbound and outbound ridership at rail transit stations.