Walking, Jogging, and Cycling: What Differs? Explainable Machine Learning Reveals Differential Responses of Outdoor Activities to Built Environment

The development of street-based outdoor physical activities plays a vital role in improving public health issues and advancing the goals of the “Healthy China” initiative, and the built environment is widely considered a key factor in promoting such activities and urban sustainability. Existing studies have paid limited attention to the nonlinear relationships between the built environment and outdoor physical activity and have mostly focused on a single type of activity (such as walking or cycling), with few comparative analyses across different activity types. With the purpose of addressing these limitations and providing cross-sectional empirical evidence for sustainable street design and active-transport policy, this study examines streets within the Second Ring Road of Changsha and uses large-scale street-level outdoor activity trajectory data to investigate associations between built environment indicators and outdoor activity flows. A Random Forest model, followed by the application of SHapley Additive exPlanations (SHAP), is used to characterize the nonlinear associations and interactions among variables, capturing patterns relevant to sustainable mobility, public health and urban form. The results indicate the following: (1) The built environment indicators are associated with walking, jogging, and cycling in distinctly different patterns—walking shows stronger associations with population density and access to bus stops; jogging demonstrates stronger associations with the accessibility of large open spaces; and cycling is more associated with land use mix and road intersection density. (2) Nonlinear associations and threshold-like patterns commonly exist between built environment variables and activity flows, with indicators such as bus stop density and walking continuity exhibiting pronounced effects within specific intervals. (3) Interaction effects among variables contribute importantly to model predictions, especially for jogging where their influence can even exceed the main effects of individual factors. These results underscore the potential value of implementing tailored street design strategies for different activity types and provide empirical evidence relevant to health-oriented urban planning.