Method for Extracting the Backbone of Urban Population Mobility Networks From an Interday Similarity Perspective

ABSTRACT The interday similarity in urban population mobility reflects essential travel demands and stable patterns, providing valuable insights for urban planning and traffic management. This study proposes a novel method for extracting the backbone of urban mobility networks based on interday similarity. The method quantifies the interday similarity of population mobility between origin–destination pairs by incorporating changes in spatial distribution and flow intensity and extracts backbone links with statistical significance using hypothesis testing and Monte Carlo simulations. Validation with mobile signaling data from Wuhan demonstrates the method's effectiveness, outperforming three established baselines. The interday similarity backbone captured a more stable representation of urban population mobility while accurately reflecting the scale‐free property. Experiments in identifying urban activity hotspots and mobility prediction further demonstrated the advantages of this backbone in elucidating urban travel demands and patterns. This approach integrates dynamic network analysis with statistical rigor, enhancing its potential for urban management and population mobility studies.