## 3 Data and Methodology
### MGWR Model
In contrast, workday peaks are associated with longer travel durations, forming a clear bimodal pattern around 20 min, indicative of longer commutes and potential traffic congestion. Figure 3 presents the spatial distribution of ride-hailing demand in Haikou's urban area across various time periods, revealing distinct workday and holiday patterns. Overall, the downtown core consistently registers higher volumes of orders compared with the peripheral and southern districts, where demand remains relatively modest. On workdays, morning trips predominantly originate in residential neighborhoods and flow toward the central employment zones, especially along major arteries and within the urban core. As midday approaches, the focal points of activity shift toward shopping districts, schools, hospitals, and large commercial centers (Wanxiang City, No. 1 High School, and the Hainan Provincial Maternal and Child Health Hospital), signaling an increase in errands and service-related trips. Moving into the evening peak, commuting demand intensifies and gradually extends southeast, yet the core area retains notably high activity levels. Following this peak, demand subsides substantially; however, select central districts and transport hubs continue to see moderate traffic late into the night. By contrast,holidays exhibit a milder and later onset of the morning peak, with a more dispersed pattern of midday and evening trips. During these periods, popular shopping areas, tourist attractions, and recreational venues experience substantial increases in demand (the Hunan Provincial Museum and Fengxiang Wetland Park), forming a multi-centered spatial structure, and the evening peak often persists into the late-night hours, underscoring an elevated level of nightlife activity. ### Variable Selection Results

In the model construction process, a combination of bidirectional stepwise regression and VIF tests were used to dynamically optimize the significance of the variables and address multicollinearity issues [96]. For all 10 dependent variables across the different time periods, significant variables were selected, as shown in Table 2. Most of the retained explanatory variables were statistically significant, and all the VIF values were below 10,

Figure 3: Average hourly count of ride-hailing usage in Haikou during ten divided time slots. Figure 2: Trip variations and trip durations of ride-hailing trips in Haikou city. with the majority even below 5, indicating low multicollinearity between the variables and ensuring the robustness and interpretability of the model [60]. The selected variables were then input into subsequent models, ensuring comparability across different model results. The analysis results show that there are significant differences in user travel demand across different time periods, and the significant explanatory variables associated with travel demand also vary by time period. Overall, the selected significant variables effectively reflect the user demand for ride-hailing services across the ten periods. By analyzing the relationship between travel demand and variables using linear regression models, the results showed that the number of significant variables on workdays was eight in the morning peak, nine during the day, eight in the evening peak, nine at night, and nine during the overnight period. During holidays, the numbers of significant variables were six, nine, eight, seven, and nine, respectively. Specifically, in the morning peak on workdays, road mileage, the number of bus stations, the proportion of buildings, greenery, traffic control facilities, and sidewalks derived from street-view images showed a significant positive correlation with the use of ride-hailing services, while the distance to the city center and the proportion of sky showed a significant negative correlation.