## 3 Data and Methodology
### MGWR Model
The presence of both large positive and negative coefficients of greenery further supports the hypothesis that greenery interacts with land-use intensity, positively associated with ride-hailing in central business areas but negatively in more walkable residential districts. In the evening peak, the significant variables include distance to the city center, bus stops, financial and auto repair POIs from the land-use category, and street-view features such as greenery, sky, roads, and traffic control facilities (Figure 5). The coefficient distribution for distance to the city center is similar to that of the morning peak, further amplifying its negative effect on ride-hailing demand. The impact of bus stops remains positive throughout the study area, emphasizing that bus stop density is closely linked to ride-hailing usage. The two land-use variables show distinct coefficient patterns: financial services have a strong positive influence in the coastal areas of Jinmao, Binhai, and Renmin Road, which may be related to local industrial patterns and increased opportunities for ride-hailing use. Conversely, auto repair services exhibit a negative correlation across the entire area (\(-4.105\) to \(-1.110\)), suggesting that areas dominated by auto repair services reduce reliance on ride-hailing. Among the street-view features, the effect of greenery is similar to that in the morning peak, with a positive correlation centered on the central urban districts and spreading outward but with more pronounced negative coefficients in several residential sectors, particularly the southern and southeastern peripheries, indicating that, unlike morning commuting, which is often time-sensitive and destination-specific, return trips may not generate the same level of urgency, especially in quieter, green neighborhoods. The proportion of sky elements shows a negative correlation globally, with minimal variation (\(-2.864\) to \(-2.659\)), while road elements show a positive correlation with little variation (\(3.760\) to \(3.911\)). The coefficient distribution for traffic control facilities differs notably from the morning peak, now primarily concentrated in the central urban areas, with negative correlations in the suburban areas, likely due to more open spaces and less traffic congestion in these areas. #### 4.5.2 Holidays

During holidays, as shown in Figure 6, significant variables for the morning peak include population, distance to the city center, bus stops, and street-view features such as buildings, greenery, and sky. The population variable, bus stops, and building elements are positively correlated across the entire area, while distance to the city center and sky elements are negatively correlated. Notably, the population variable shows a peak concentration in Meilan District, a residential area in Haikou located farther from the city center, which increases the reliance on ride-hailing. The peak area for bus stops shifts to the southeast, away from the central urban area, indicating that, on holidays, users are more likely to combine public transportation with ride-hailing for their travel needs. The coefficient

Figure 4: Coefficient values of ride-hailing usage determinants during workday morning peak. Figure 5: Coefficient values of ride-hailing usage determinants during workday evening peak. distribution for buildings shows two centers: the western Jinmao and Binhai Streets, and the eastern Lantian and Guoxing Streets, which are Haikou's financial and cultural districts, respectively, generating different travel demands. The distribution of greenery is similar to that of workdays but more dispersed, with an expanded positive impact, reflecting the more diverse nature of holiday travel purposes, such as leisure, shopping, or family visits. In some areas, higher greenery coverage enhances travel comfort and the attractiveness of destinations, thus encouraging ride-hailing use, while, in others, the same factor may coincide with low activity intensity and high self-sufficiency, suppressing mobility demand.