This study examines how Free-Floating Bike-Sharing (FFBS) affects the accessibility equity of public transit sytems by serving as a first-mile feeder. To evaluate accessibility improvements for various opportunities within a 30-min travel time, we construct a complete travel chain approach based on multi-source, real-world data from Nanjing, China. The results indicate that FFBS significantly enhances accessibility, particularly for job opportunities and green spaces, with improvements of up to 180.02% and 155.82%, respectively. This integration also enhances the accessibility equity of public transit systems, particularly in green spaces, with a Gini coefficient improvement of 0.0336. Additionally, we find that areas with low housing prices exhibit greater accessibility inequality, while those with moderate housing prices benefit more from FFBS integration. These findings can potentially support transport planners in optimizing and managing FFBS and public transit systems to facilitate sustainable and inclusive transportation networks.

The high-speed railway (HSR) has profoundly influenced individuals’ lifestyles and travel behaviors. The development of HSR stations and their surrounding areas plays a critical role in urban growth, enhancing both transport efficiency and urban functionality. This study investigates the development of HSR station areas, with a particular focus on Shanghai Hongqiao station, emphasizing the enhancement of street vitality as essential for integrated urban development. Street vitality in station areas is closely associated with individuals’ activities and travel behaviors, influenced by walking accessibility and the built environment. Understanding these factors is crucial for improving the efficiency and attractiveness of HSR station areas. Although extensive research has examined the separate impacts of the built environment and walking accessibility on street vitality, a significant gap remains in comprehending their synergistic effects. This study employs GPS and point-of-interest (POI) data to analyze the stay time of HSR passengers in station areas. Utilizing machine learning algorithms and geographic information system (GIS) tools, this research models the impact of walking accessibility and the built environment on passengers’ stay time. The results indicate that passengers are more inclined to remain within areas accessible by a 7 min walk from the station. Furthermore, the synergistic effects of walking accessibility and the built environment can inform the spatial planning of various functions. These findings provide valuable insights for urban planners and policymakers aiming to enhance the development and efficiency of HSR station areas.

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.