Predicting Urban Vitality at Regional Scales: A Deep Learning Approach to Modelling Population Density and Pedestrian Flows

Understanding and predicting urban vitality—the intensity and diversity of human activities in urban spaces—is crucial for sustainable urban development. However, existing studies often rely on discrete sampling points and single metrics, limiting their ability to capture the continuous spatial distribution of urban vibrancy. This study introduces the UVPN (urban vitality prediction network), a novel deep-learning architecture designed to generate high-resolution predictions of static and dynamic vitality at regional scales. The architecture integrates two key innovations: a SE (squeeze-and-excitation) block for adaptive feature recalibration and an RCA (residual connection with coordinate attention) bottleneck for position-aware feature learning. Applied to New York City, UVPN leverages diverse urban morphological features such as streetscape attributes and land use patterns to predict continuous vitality distributions. The model outperforms existing architectures, achieving reductions of 34.03% and 38.66% in mean squared error for population density and pedestrian flow predictions, respectively. Feature importance analysis reveals that road networks predominantly influence population density, while streetscape features strongly affect pedestrian flows, with built density and points of interest contributing to both dimensions. By advancing urban vitality prediction, UVPN provides a robust framework for evidence-based urban planning, supporting the creation of more sustainable, functional, and livable cities.