Nonlinear and Interactive Effects of the Built Environment on Low‐Carbon Travel Intentions: Evidence From Large‐Scale Map Usage Data in Beijing

Abstract

Understanding the relationship between travel behavior and modifiable built environment attributes is essential for promoting low‐carbon urban mobility, particularly under emerging carbon peaking and neutrality targets. While previous studies have explored this relationship, limited attention has been paid to residents’ intentions for low‐carbon travel modes. To address this gap, this study employs large‐scale, anonymized map usage data from Beijing and applies a gradient boosting decision trees (GBDT) model to examine the nonlinear and interaction effects of built environment attributes on behavioral intentions at both trip origins and destinations. The results indicate that destination road density exerts the strongest influence on low‐carbon mode choices, whereas factors such as scenery density and residential density display notable threshold effects. Furthermore, strong interaction effects between residential density and living service density highlight the importance of integrated urban planning to facilitate sustainable mobility. Model validation demonstrates that the GBDT approach outperforms both random forest and multinomial logit models, achieving superior predictive accuracy (85.7%) and effectively capturing complex nonlinear relationships. These findings offer actionable insights for policymakers: interventions should prioritize enhancing road network density up to 18.5 km/km 2 , fostering medium‐density residential areas (10–35 units/km 2 ), and integrating comprehensive living services within neighborhoods. Overall, this study contributes a reliable, data‐driven evidence base to inform targeted urban transport planning and land‐use management for advancing low‐carbon urban development.