Percolation transition of K-destinations reveals universal backbone of urban mobility networks

Abstract

Abstract Cities are held together by a small subset of recurrent origin–destination ties, but extracting this backbone from noisy mobility data remains difficult, and most models still prioritize distance over connectivity. We build directed mobility networks from 48 months of mobile-phone data across eight US cities (2018–2021) and apply a rank-based percolation filter that retains, for each origin, its top-K destinations. Defining K∗ as the smallest cutoff that yields a strongly connected network, we find that city-specific thresholds cluster tightly across space and time, with a representative value near K≈130. Degree-preserving and gravity-style baselines do not recover this scale, underscoring the importance of selectively preserved long-range functional ties. We then define PPD(K), an origin-level mobility concentration measure, and show that socioeconomic associations peak around the percolation-derived integration regime. Together, these findings offer a principled method to extract a minimal mobility backbone and connect its structure to persistent socioeconomic variation.