Differentiated impacts of urban streetscapes on green mobility experiences

Against the backdrop of accelerating urbanization and the growing emphasis on healthy city development, understanding the differentiated impacts of street spatial environments on human mobility and their perceived friendliness is essential for integrating green mobility strategies, optimizing street design, and enhancing public health outcomes. This study investigates the urban street environment of Nanjing, Jiangsu Province, by integrating streetscape imagery with interpretable machine learning models. Taking the perspectives of both pedestrians and cyclists, the research identifies mode-specific perceptual differences in mobility experience. The results indicate that pedestrian-friendly perceptions are primarily influenced by the proportion of visible vegetation, measured as the Green View Index (39.6%), and the Sky View Factor (9.4%). In contrast, cyclist-friendly perceptions are more strongly shaped by the proportion of the cyclist’s visual field (20.9%), the presence of motor vehicles, and street openness. Both the Green View Index and street openness emerge as critical positive factors jointly enhancing the walking and cycling experience. Additionally, using methods based on space syntax theory, the study found that areas characterized by both high accessibility and strong mobility friendliness perceptions are primarily concentrated around major urban green spaces and their surrounding areas.