## 6 Conclusions  
Based on the FOI data and AFC data around the stations, this paper explores the influence mechanism of passenger flow and the surrounding built environment factors in Shenzhen rail transit stations. Spatial autocorrelation, multicollinearity, and stepwise regression are used to filter out the key independent variables affecting station patronage. The GWR model, which considers average housing prices as an independent variable, is found to be more accurate in predicting passenger flow. This conclusion is further verified by comparing the prediction results of the OLS and GWR models for different types of stations. Taking administrative districts as the perspective, the spatial heterogeneity of the influence of these factors on rail traffic flow is analyzed by visualizing the local regression coefficients of each influential factor in the GWR model. Based on the results of this study, policy recommendations for urban transportation planning and housing price regulation in Shenzhen are proposed:  
1. Average house prices have a global negative correlation effect on ridership. Lower housing prices are usually accompanied by higher metro ridership. Nowadays, with the increase in migrant workers and passenger flow in Shenzhen, the government should reasonably regulate the level of housing prices to balance the demand of residents for housing and transportation resources. 2. Based on OLS and GWR model calculations, and visualizing the local regression coefficients of each influential factor in the GWR model, the following is evident: the degree of influence of the influential factors in descending order is the building volume ratio, accessibility, commercial residences, and the average house price. In addition to average housing prices, which have a global negative correlation with the morning peak passenger flow, the building volume ratio has a global positive correlation, while accessibility has a global negative correlation. Government and corporate offices, as well as commercial housing, have significant spatial heterogeneity effects on Shenzhen rail transit passenger flow. 3. The building volume ratio reflects the intensity of land use and the level of development of the built environment [57]. By visualizing the local coefficients of the building volume ratio, the spatial characteristics of Shenzhen's built environment are revealed, showing a shift from industrial areas in the east to commercial and residential structures in the west, from the perspective of each administrative district. The shortcomings of this paper are that, although this paper puts forward policy recommendations on urban transportation planning and housing price regulation based on built environment factors, relevant policy factors were not directly quantified or incorporated into the analytical framework during model construction. At the same time, for a city like Shenzhen, which is characterized by both intra-city short-distance commuting and cross-city long-distance commuting, this study does not adequately discuss the impact of the characteristics of travel behaviors on passenger demand under this special commuting mode. In future research, AFC data can be further identified based on POI, and policy factors and travel characteristics can be quantified and included in the analysis to predict passenger flow concerning the regional policies and travel behavior characteristics of Shenzhen. Moreover, the influence of the built environment and other variable factors on passenger flow heterogeneity at rail transit stations during different periods (e.g., morning and evening peaks, weekdays, weekends, holidays) can also be considered to provide a theoretical basis for better urban transportation planning and development.