## 3 Data and Methodology
### MGWR Model
This suggests that the demand for travel during the morning peak is concentrated in areas with higher traffic convenience, larger building densities, and better greenery and walking facilities, which aligns with commuting-focused travel characteristics. Over time, the correlation with road mileage, bus stations, and traffic control facilities gradually strengthened, and the regression coefficient for financial-related places became significant, positively correlating with ride-hailing service usage. Meanwhile, the correlation with distance to the city center, the number of auto repair shops, greenery ratio, and sky ratio weakened, with the direction of some variable correlations even changing (e.g., the greenery ratio shifted from a positive to a negative correlation). During the evening peak, the concentration of traffic facilities and the regression coefficient for financial-related places peaked, indicating that, at this time, travel demand is more concentrated in the central urban area. Work-related traffic facilities and places have a significant positive impact on ride-hailing service use, while greenery and sky ratios significantly suppress travel demand. During the night and overnight periods, the correlation with traffic facilities and financial-related places began to weaken, while the correlation with the number of auto repair shops, greenery ratio, and sky ratio gradually strengthened, with a reduced demand for commuting. Further analysis also revealed that the effect of housing prices was not significant during the morning and evening peaks but showed some significance during the day, night, and overnight periods, reflecting the temporal dependency of housing prices on travel demand. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Wuxiliary**} & \multicolumn{10}{c}{**Holiday**} \\ \cline{2-13}
**Variable** & \multicolumn{3}{c}{**Monzing Peak**} & \multicolumn{3}{c}{**Monzing Day**} & \multicolumn{3}{c}{**Evening Peak**} & \multicolumn{3}{c}{**Evening**} & \multicolumn{3}{c}{**Overnight**} & \multicolumn{3}{c}{**Monzing Peak**} & \multicolumn{3}{c}{**Missing Day**} & \multicolumn{3}{c}{**Evening Peak**} & \multicolumn{3}{c}{**Evening**} & \multicolumn{3}{c}{**Overnight**} \\ \cline{2-13}  & \multicolumn{1}{c}{**Coeff. **} & \multicolumn{1}{c}{**VIF**} & \multicolumn{1}{c}{**Coeff. **} & \multicolumn{1}{c}{**VIF**} & \multicolumn{1}{c}{**Coeff. **} & \multicolumn{1}{c}{**VIF**} & \multicolumn{1}{c}{**Coeff. **} & \multicolumn{1}{c}{**VIF**} & \multicolumn{1}{c}{**Coeff. **} & \multicolumn{1}{c}{**VIF**} & \multicolumn{1}{c}{**Coeff. **} & \multicolumn{1}{c}{**VIF**} & \multicolumn{1}{c}{**Coeff. **} & \multicolumn{1}{c}{**VIF**} & \multicolumn{1}{c}{**Coeff. **} & \multicolumn{1}{c}{**VIF**} & \multicolumn{1}{c}{**Coeff. **} & \multicolumn{1}{c}{**VIF**} & \multicolumn{1}{c}{**Coeff. **} & \multicolumn{1}{c}{**VIF**} & \multicolumn{1}{c}{**Coeff. **} & \multicolumn{1}{c}{**VIF**} \\ \hline Population & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(0.404^{\star}\) & \(1.642\) & \(0.570^{\star}\) & \(1.648\) & \(-\)



### Results of Spatial Autocorrelation Test

While the linear regression model highlights the relationships between variables and ride-hailing demand, its limitations must be acknowledged. First, the linear regression model assumes independence between observations without accounting for the spatial aggregation or diffusion properties of the dependent variable (i.e., spatial autocorrelation) [53]. In reality, ride-hailing demand is influenced not only by the characteristics of the current region but also by potential spillover effects from neighboring areas, which might show spatial dependence. Second, the model assumes that the regression coefficients are consistent across the entire study area, yet the influence of variables often varies significantly by spatial location.