Policy makers have long been interested in detecting 'high-emitters', a supposedly smallfraction of vehicles that make disproportionally large contributions to total fleet emissions. However, existing identification schemes often exclusively rely on snapshot measurements (i.e. emissions within less than a second), and thus simply identify vehicles with high instantaneous emissions, instead of vehicles with high average emissions over a driving period as regulated by emission standards. We design a comprehensive scheme to address this challenge by combining fleetwide remote sensing measurements with detailed second-by-second emission measurements from individual vehicles. We first determine the trip-average NO x emission rates of individual vehicles in a Euro-5 diesel fleet measured across European locations; this allows, second, to calculate the fraction and emission contributions of high-emitters based on trip-average emission. We demonstrate that the identification of high-emitters is quite uncertain as long as it is based on single snapshots only; but 80% of the high-emitters can be identified with over 75% precision with five or more repeated measurements of the same vehicle. Compared to the conventional detection schemes, our scheme can increase the identified high-emitters and associated emission reductions by over 140%. Our method is validated and shown to be superior to the conventional interpretation of snapshot measurements.