The ability of any nation to support economic growth and commerce relies on their capacity to preserve and to sustain the performance of pavement assets. The ever-widening funding gap to maintain pavements challenges the scaling of existing techniques to measure ride quality. The international roughness index is the primary indicator used to assess and forecast maintenance needs. Its fixed simulation procedure has the advantage of requiring relatively few traversals to produce a consistent characterization. However, the procedure also underrepresents roughness that riders experience from spatial wavelengths that fall outside of the model’s sensitivity range. This paper introduces a connected vehicle method that fuses inertial and geospatial position data from many vehicles to expose roughness experienced from all spatial wavelengths. This study produced both roughness indices simultaneously from the same inertial profiler. The statistical distribution of their ratios agreed with a classic t-distribution. The two indices collected from three different pavement sections at two different speeds exhibit a direct proportionality within a margin-of-error that diminished below 2% as the extrapolated traversal volume approached 100. Practitioners are currently evaluating the connected vehicle method to implement lower-cost and more scalable alternatives to the international roughness index.