## 3 Data and Methodology
### MGWR Model
In the evening peak on holidays, the significant variables include distance to the city center, bus stops, financial and auto repair services, and street-view features such as buildings, sky, roads, and traffic control facilities (Figure 7). The coefficient distribution for distance to the city center changes from the hierarchical pattern of the morning peak to the lowest point in the central urban area, rising gradually outward, with more distinct spatial variation. Globally, the negative correlation remains, indicating that users are more sensitive to distance from the city center at this time, with a more negative attitude. The distribution for bus stops also shifts, with the peak areas now concentrated in Haifu, Heping, Lantian, Guoxing Streets, and Haikan Street on the west side, showing positive correlations in the central area and negative correlations in the peripheral areas, which may be related to holiday travel patterns. The effect of financial services diminishes compared to workdays, while the negative impact of auto repair services on ride-hailing usage intensifies. The positive impact of buildings is weaker in the evening peak, and the distribution becomes smoother (0.440 to 0.693), suggesting that users are less likely to rely on ride-hailing in developed urban areas during this period, with the remaining variables showing similar coefficient distributions to those of workdays, with little spatial variation. Figure 6: Coefficient values of ride-hailing usage determinants during holiday morning peak. ## 5 Discussion and Conclusions

As an emerging mode of transportation, the relationship between ride-hailing demand and the built environment has attracted increasing attention from researchers, policymakers, and platform operators [17; 19; 24]. However, due to data limitations and methodological constraints, accurately identifying the key determinants of ride-hailing demand at the urban scale remains challenging. This study addresses this gap by integrating multi-source heterogeneous data, including POIs, socioeconomic indicators, and street-view imagery, to examine the spatiotemporal impacts of the built environment on ride-hailing demand in Haikou, China. Using a one-month dataset at a 0.5 km \(\times\) 0.5 km grid level, we extract 26 variables across three categories and apply global and local spatial models to analyze their effects. Our findings reveal substantial spatial and temporal heterogeneity in ride-hailing demand, with distinct influencing factors emerging across different time periods and urban zones. Among the models tested, the multi-scale geographically weighted regression (MGWR) model demonstrates superior explanatory power, effectively capturing local variations and resolving scale differences among variables. For instance, proximity to the city center and density of bus stops are consistently influential, although their effects vary by time and location. Land-use types such as financial services and auto repair areas show divergent patterns, and street-view features, especially the proportions of greenery, buildings, and sky, further illuminate the role of micro-scale environmental aesthetics in shaping mobility preferences. These results offer important implications for urban transportation planning and policy. First, they suggest that ride-hailing infrastructure, such as designated pick-up and drop-off zones, should be strategically located based on spatial clusters of demand and the local built-environment characteristics. Second, the findings on the time-sensitive effects of specific land uses and street features can inform adaptive traffic management strategies, particularly during holidays or peak periods. Finally, the integration of street-level visual indicators provides a novel and scalable approach for assessing mobility equity and service coverage, especially in underserved peripheral regions.