## 3 Data and Methodology
### MGWR Model
In the overnight period, spatial diffusion effects become more prominent, with larger MGWR bandwidths, such as for distance from the city center and buildings, indicating that late-night demand is more influenced by the overall urban space structure rather than local factors. Additionally, holiday demand is more dispersed, and the spatial effect range of variables is larger than during workdays. For instance, the bandwidth for greenery rate and building density is larger during holidays, suggesting that holiday travel patterns are influenced more by leisure and living environment factors than by commuting needs. MGWR dynamically adjusts the spatial effect range of different variables, providing a more accurate depiction of the variations in ride-hailing demand across regions and times. ### Local Estimates in the MGWR Model

In this section, we analyze the significant explanatory variables of ride-hailing demand across different time periods using the MGWR model. The results are presented in Table 8, summarizing the distribution and variability of the parameter estimates across the time periods. We then visualize the spatial distribution of these coefficients, focusing on the morning and evening peak hours of workdays and holidays, to further illustrate the spatiotemporal dynamics of the explanatory factors. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Weakday**} & \multicolumn{10}{c}{**Heliagy**} \\ \cline{2-13}
**Variable** & \multicolumn{3}{c}{**Mening Peak**} & \multicolumn{3}{c}{**Mening Day**} & \multicolumn{3}{c}{**Ewing Peak**} & \multicolumn{3}{c



\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{13}{c}{**Variable**} & \multicolumn{13}{c}{**Variable**} & \multicolumn{13}{c}{**Hability**} \\ \cline{2-13} \multicolumn{13}{c}{**Variable**} & \multicolumn{13}{c}{**Moning Peak**} & \multicolumn{13}{c}{**Eveing Peak**} & \multicolumn{13}{c}{**Moning Peak**} & \multicolumn{13}{c}{**Eveing Peak**} & \multicolumn{13}{c}{**Eveing Peak**} \\ \cline{2-13} \multicolumn{13}{c}{**Mean**} & **Sid** & **Min** & **Median** & **Max** & **Mean** &



#### 4.5.1 Workdays

Figure 4 illustrates the distribution of correlation coefficients for eight explanatory variables in the workday morning peak. These variables include distance to the city center, road mileage, bus stops (from the socioeconomic and transportation accessibility categories), and street-view features such as buildings, greenery, sky, traffic control facilities, and sidewalks. It is observed that the distance to the city center generally shows a negative correlation with ride-hailing usage, with minimal spatial variation (\(-1.106\) to \(-1.052\)). In contrast, road mileage and bus stops both have positive coefficients, indicating that better transportation accessibility attracts more demand for ride-hailing services. These variables exhibit similar distribution patterns, centered around Jinyu and Fucheng Streets, extending towards the two main roads, Ye Hai Avenue and Yingbin Avenue, supported by the well-developed transport networks around Haikou East Station and Haikou Passenger Station, which significantly enhance ride-hailing demand in the morning peak. The traffic control facilities exhibit a distribution pattern similar to that of bus stops, while the proportion of sky elements shows a negative correlation with ride-hailing demand, particularly pronounced in the central urban districts (Binhai, Jinmao, Haifu, and Datong Streets). At the same time, the coefficient distribution of the buildings in these regions shows positive peaks, suggesting that users in developed urban areas are more likely to use ride-hailing, supported by well-maintained greenery and sidewalk designs.