## 5 Results and Discussion
### Spatial Heterogeneity of Local Influence Factor Coefficients in GWR Model  
The GWR model analysis allows us to obtain the coefficients for each site, and we can observe the spatial distribution of and variation in these coefficients through the spatial structure map. To study the spatial heterogeneity of each influencing factor on the morning peak passenger flow in Shenzhen, this paper visualizes the distribution \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{3}{c}{**OLS**} & \multicolumn{2}{c}{**GWR**} \\ \cline{2-5}  & **Station** & **AE** & **RE** & **AE** & **RE** \\ \hline \multirow{3}{*}{**Commercial housing**} & Tangkeng & 1310 & 17.33\% & 686 & 9.07\% \\  & Meicun & 820 & 11.65\% & 661 & 9.39\% \\  & Gangxia & 2807 & 15.89\% & 1044 & 5.91\% \\  & Shixia & 1648 & 12.40\% & 224 & 1.69\% \\ \hline \multirow{3}{*}{**Goverment and corporate offices**} & Science Museum & 2273 & 20.21\% & 1066 & 9.48\% \\  & Shawei & 2116 & 14.07\% & 314 & 2.09\% \\  & Caopu & 1507 & 16.87\% & 875 & 9.80\% \\  & Henggang & 1270 & 13.18\% & 216 & 2.24\% \\  & Children’s Palace & 1017 & 10.10\% & 62 & 0.61\% \\  & University Town & 1999 & 12.58\% & 904 & 5.69\% \\ \multirow{3}{*}{**Transportation hub**} & Qianhaiwan & 840 & 18.26\% & 667 & 14.50\% \\  & Airport & 509 & 12.94\% & 73 & 1.85\% \\ \cline{1-1}  & & & 4266 & & 3021 \\ \cline{1-1}  & & & 77.57\% & & 46.63\% \\ \cline{1-1}  & RMSE & & 5610 & & 4147 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of model prediction results. of the local coefficients of each influencing factor by ArcMap10.8, which reacts to the spatial heterogeneity of the influencing factors' impacts on the rail transit passenger flow. Since Shenzhen is a multi-cluster center city, and its structure is mainly divided into regions [51], this paper discusses each administrative district of Shenzhen as a unit, as shown in Figures 6-10. Figure 6: Spatial distribution of local coefficients of average house price. Figure 7: Spatial distribution of local coefficients of government and corporate offices. Figure 8: Spatial distribution of local coefficients of commercial housing. Figure 9: Spatial distribution of local coefficients of floor area ratio. From the analysis in Figure 6, it is clear that the average house price has a globally negative impact on the morning peak passenger flow. This may be because there is little structural difference in the built environment of Shenzhen regions in general. Regions with high house prices have diversified transportation facilities, and residents may choose to travel by private car; according to the short one-way average morning peak distance in Shenzhen, those with a close morning peak distance may choose to travel on foot or by bicycle. The majority of migrant workers with low and middle income levels will choose to live in areas with low house prices, which can reflect the current situation of residents' transportation in Shenzhen. The analysis in Figure 7 shows the spatial heterogeneity of the influence of government and corporate office locations on the passenger flow of Shenzhen rail transit. The western part of the Futian District has a higher influence coefficient due to its proximity to the core commercial CBD, while the eastern industrial area is less affected by the passenger flow despite its proximity to the commercial area. Extending southwestward to the Nanshan, Longhua, and Bao'an Districts, the positive impact coefficient decreases, and the impact of the northern section of Bao'an picks up due to its proximity to Dongguan, reflecting the attractiveness of the office-dense area to commuter flows. Extending northeastward to the Luohu and Longgang Districts, the negative impact coefficient increases, indicating that the high level of automation and fewer office workers in these industrial districts have a limited impact on rail traffic flow. The analysis in Figure 8 shows the spatial heterogeneity of the influence of commercial housing on the passenger flow of Shenzhen rail transit.