Beyond static brightness: daily nighttime light fluctuations enrich nighttime vitality evaluation for urban zones

• NTL intensity and fluctuation jointly characterize nighttime vitality • The NLFI quantifies meaningful NTL fluctuations while excluding background noise • NLFI reveals hotspots and spatial gradients of nighttime activity dynamics • NLFI is consistently associated with urban functions across megacities • Built-environment factors shape distinct nighttime vitality patterns Urban nighttime vitality significantly contributes to a city’s economic, social, and cultural attractiveness. This study develops a dual-perspective framework that integrates annual mean nighttime light (NTL) intensity indicating stable infrastructural capacity, with a novel Nighttime Light Fluctuation Index (NLFI) capturing the dynamic pulse of human activity. Rather than measuring vitality directly, the framework extends intensity-based vitality evaluations by incorporating temporal dynamics, enabling the categorization of urban zones by distinct vitality-related patterns. Applying the framework to 25 global megacities, with detailed analyses in Los Angeles and Beijing, we identified three distinct patterns: “Bright-Fluctuating”, “Bright-Steady”, and “Low-Lit” zones. A consistent cross-city association was found between these patterns and urban functions: residential areas consistently exhibit low fluctuation, while commercial, industrial, and transportation hubs are hotspots of high fluctuation. Spatially, fluctuation hotspots in Los Angeles are concentrated in its central commercial and activity hubs, whereas in Beijing, they are also prominent in newly developing areas in the urban periphery, unveiling different urban dynamics and development. An XGBoost model combined with SHapley Additive exPlanations (SHAP) analysis revealed the key built-environment drivers behind these patterns. In Los Angeles, functional diversity and density (e.g., non-residential land use) are the primary drivers. In contrast, Beijing’s patterns are predominantly shaped by infrastructure, with distance to the metro network being the most critical factor. These findings demonstrate the value of capturing NTL’s temporal dynamics in understanding the interplay between built environment and nighttime urban dynamics, offering a powerful tool for urban planning and policy.