The relationship between green spaces and active travel has been extensively studied. However, the majority of previous studies relied on small datasets concerning active travel and inadequately explored non-linear and/or synergistic effects. This study uses multi-source data and interpretable machine learning techniques to identify the non-linear and synergistic effects of green spaces in Chengdu (China) on two types of active travel: cycling and running. Crowdsourced data from Strava collected in December 2021 is used to measure city-wide active travel levels. Meanwhile, green spaces are evaluated from two viewpoints: overhead view and eye level, with the latter assessed using Baidu Street View imagery. The findings demonstrate that green spaces can account for up to 20% of the variance in active travel. Generally, the effect of the area of green spaces on active travel is positive. When the area of green spaces reaches a certain threshold, its effect becomes marginal and even negative. The green view index displays complex effects on cycling. Furthermore, this study identifies synergistic effects among predictors (e.g., green view index & river line length).