This study applies sigmoidal models to predict pavement condition index (PCI) deterioration over time for residential and non-residential (main, collector and industrial) streets using pavement condition data from Skellefteå Municipality, Sweden. The dataset includes windshield-survey pavement assessments conducted in 2014, 2018 and 2022. Two modelling configurations were applied: temporal models calibrated using 2014 and 2018 observations to predict 2022 PCI ratings, and full dataset models developed using all available observations to represent overall deterioration behaviour. Separate sigmoidal curves were developed for residential and non-residential streets, as well as for the specific maintenance-treatment categories applied exclusively to non-residential streets. Sigmoidal curves for residential streets showed lower predictive performance and higher variability, potentially associated with heterogeneous pavement conditions, inconsistent pavement age and localized utility cuts, whereas sigmoidal curves for non-residential streets demonstrated comparatively better predictive capability due to more consistent deterioration patterns. Model performance also varied across maintenance-treated pavement surfaces, partly due to limited sample sizes. Sigmoid best-fit curves for street pavements treated with special treatments (ST) showed the highest predictive performance, while milling and resurfacing (MR)-treated pavements demonstrated relatively good agreement with measured PCI ratings. Surface levelling (SL) is a relatively low-cost treatment and may be suitable for earlier stages of deterioration, although traffic and pavement characteristics should also be considered when selecting treatments. Differences in unit cost between treatment types are considered only for contextual interpretation. Overall, sigmoidal curves may support more timely maintenance planning in data-limited municipal pavement management and reduce the need for major reconstruction.