## 4 Results
### Results of the Regression Model  
This study examines the factors influencing predetermined urban dynamic measurements at three distinct time intervals: 2016, 2018, and 2020. We used multiple linear regression analysis to evaluate the significance and impact of variables, including the quantity of catering POIs and high-traffic commercial POIs, and their popularity metrics within an 800-m radius. The regression model incorporated both non-standardized and standardized coefficients to quantify the effects of each predictor variable. This approach allowed us to assess the influence of these factors on the studied outcomes. The standardized coefficient (Beta) facilitated the comparison of the relative influence of predictor variables across different scales. The significance level (\(p\)-value) was utilized to validate the statistical relevance of each predictor variable, while the variance inflation factor (VIF) was applied to evaluate multicollinearity (Figure 5). Figure 4: The attractiveness of urban rail transit stations to nearby POIs with high traffic. As depicted in Figure 5, the histogram (1) exhibits an approximately bell-shaped distribution, with minor deviations attributable to the influence of a limited number of stations. The P-P plot (2) reveals that the majority of metro stations align with the expected distribution, as the points predominantly form a near-straight line, with only slight deviations at the extremes. These observations collectively indicate that the residuals of station hotness adhere to a normal distribution, a condition that sufficiently validates the normality of the sample data despite the small sample size. Figure 5 further demonstrates the robustness of the regression model, confirming that the number of highly visited POIs within an 800-m radius of metro stations, the overall hotness, and the count of catering POIs serve as significant explanatory variables for metro station hotness. These explanatory variables are further substantiated through an analysis of the data presented in Table 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{5}{c}{**ANOVA a**} \\ \hline \multicolumn{5}{c}{**The Sum of Squares**} & **Df** & **Mean Square** & **F** & **Sig.** \\ \hline Regression & 20.775 & 3 & 6.925 & 20.853 & 0.000b \\ \hline Residual & 3.985 & 12 & 0.332 & & \\ \hline Total & 24.760 & 15 & & & \\ \hline \multicolumn{5}{c}{Dependent variable: ridership volume of the subway system} \\ Predictor variables: (constant), intra-area quantity of commercial POIs, intra-area quantity of high-frequency POIs, total heat of POIs \\ \hline \multicolumn{5}{c}{coefficient} \\ \hline \multicolumn{5}{c}{Unstandardized coefficient} \\ \hline Model & B & Std. Error & Beta & t & Std. \\ \hline \multicolumn{5}{c}{(Constant quantity)} & 1.031 & 0.359 & 2.875 & 0.014 \\ \hline \multicolumn{5}{c}{The intra-area quantity of the commercial point of interest (POI)} \\ \hline \multicolumn{5}{c}{The intra-area quantity of the high-frequency point of interest (POI)} \\ \hline \multicolumn{5}{c}{The total heat of the point of interest (POI)} \\ \hline \multicolumn{5}{c}{\(-\)0.006} \\ \hline \end{tabular}
\end{table} 
Table 2: Multiple Relations Coefficients. Figure 5: Histograms and P-P distribution plots were analyzed by linear regression. The analysis presented in Table 2 indicates that the quantity of commercial POIs within an 800-m radius exhibits a statistically significant P value and a substantial Beta coefficient, thereby demonstrating a pronounced influence on the traffic volume at the subway station. This highlights the crucial role of commercial POIs in enhancing subway station vitality. The Beta coefficients for the number of high-traffic POIs and their heat value are relatively similar. This suggests that both variables comparably influence the station's traffic volume.