Incorporating Street-View Imagery into Multi-Scale Spatial Analysis of Ride-Hailing Demand Based on Multi-Source Data

The rapid expansion of ride-hailing services has profoundly impacted urban mobility and residents’ travel behavior. This study aims to precisely identify and quantify how the built environment and socioeconomic factors influence spatial variations in ride-hailing demand using multi-source data from Haikou, China. A multi-scale geographically weighted regression (MGWR) model is employed to address spatial scale heterogeneity. To more accurately capture environmental features around sampling points, the DeepLabv3+ model is used to segment street-level imagery, with extracted visual indicators integrated into the regression analysis. By combining multi-scale geospatial data and computer vision techniques, the study provides a refined understanding of the spatial dynamics between ride-hailing demand and urban form. The results indicate notable spatiotemporal imbalances in demand, with varying patterns across workdays and holidays. Key factors, such as distance to the city center, bus stop density, and street-level features like greenery and sidewalk proportions, exert significant but spatially varied impacts on demand. These findings offer actionable insights for urban transportation planning and the design of more adaptive mobility strategies in contemporary cities.