Abstract Active school commuting makes a vital contribution to physical activity, thus improving the health and well-being for children and adolescents. The built environment is widely acknowledged as a factor that can affect travel behavior. However, few studies assess the influence of micro-level streetscape features on active school commuting using street view images. Additionally, existing research often presupposes that the relationship between built environment and walking to school is linear or generalized linear. Using data from the 2016 census and street view images in Hong Kong, this paper investigates the non-linear relationship between the micro-level streetscape features and propensity of walking to school with several machine learning methods. A refined evaluation of streetscape features is conducted through semantic segmentation, object detection and perceptual color analysis. The results show that the non-linear model facilitates a deeper understanding of the genuine relationship between streetscape features and propensity of walking to school. Grass view index, mean saturation of buildings and number of traffic lights have greater significance in predicting walking propensity. Moreover, some of the streetscape features have salient threshold effects, indicating that environmental interventions would only be effective within a specific range. These findings can provide nuanced and fine-grained guidance for building a walkable, children-friendly and sustainable city.

Highlights • Machine learning techniques are applied to explore the non-linear relationship. • Streetscape features exhibit salient non-linear and threshold effects on walking propensity. • The hierarchy of priorities and most effective ranges for environmental intervention are revealed.