Machine learning-based study on factors influencing street vitality in urban fringe commercial districts: a case of Wuhan

Under the context of megacity decentralization, street vitality in urban fringe commercial districts becomes a key indicator for assessing population attraction and sustainability. However, most existing studies focus on central urban areas, with limited attention to the vitality characteristics of urban fringe streets and the nonlinear relationship between the built environment and street vitality. Methods for quantifying street vitality also remain underdeveloped. This study examines streets in commercial districts located in Wuhan's urban fringe areas. Street vitality is quantified using mobile signaling data. Based on multi-source data, a “5D” built environment indicator system is constructed. Machine learning combined with SHAP algorithms is employed to reveal the nonlinear effects and interaction mechanisms of built environment variables. Finally, hierarchical clustering is used to identify different types of street vitality. The results show that: (1) Location distance is the dominant factor influencing street vitality, with time-varying effects; (2) Built environment variables affect vitality in nonlinear ways; (3) There are interactive effects among indicators of different dimensions of built environment; (4) Streets with similar contribution patterns from built environment variables tend to have similar vitality levels. Vitality formation mechanisms vary by street type and show spatial clustering. Findings support refined urban fringe street design.