Methods are needed to characterize short-term exposure to ultrafine particle number concentrations (UFP) for epidemiological studies on the health effects of traffic-related UFP. Our aims were to assess season-specific spatial variation of short-term (20-min) UFP within the city of Basel, Switzerland, and to develop hybrid models for predicting short-term median and mean UFP levels on sidewalks. We collected measurements of UFP for periods of 20 min (MiniDiSC particle counter) and determined traffic volume along sidewalks at 60 locations across the city, during non-rush hours in three seasons. For each monitoring location, detailed spatial characteristics were locally recorded and potential predictor variables were derived from geographic information systems (GIS). We built multivariate regression models to predict local UFP, using concurrent UFP levels measured at a suburban background station, and combinations of meteorological, temporal, GIS and observed site characteristic variables. For a subset of sites, we assessed the relationship between UFP measured on the sidewalk and at the nearby residence (i.e., home outdoor exposure on e.g. balconies). The average median 20-min UFP levels at street and urban background sites were 14,700 ± 9100 particles cm−3 and 9900 ± 8600 particles cm−3, respectively, with the highest levels occurring in winter and the lowest in summer. The most important predictor for all models was the suburban background UFP concentration, explaining 50% and 38% of the variability of the median and mean, respectively. While the models with GIS-derived variables (R2 = 0.61) or observed site characteristics (R2 = 0.63) predicted median UFP levels equally well, mean UFP predictions using only site characteristic variables (R2 = 0.62) showed a better fit than models using only GIS variables (R2 = 0.55). The best model performance was obtained by using a combination of GIS-derived variables and locally observed site characteristics (median: R2 = 0.66; mean: R2 = 0.65). The 20-min UFP concentrations measured at the sidewalk were strongly related (R2 = 0.8) to the concurrent 20-min residential UFP levels nearby. Our results indicate that median UFP can be moderately predicted by means of a suburban background site and GIS-derived traffic and land use variables. In areas and regions where large-scale GIS data are not available, the spatial distribution of traffic-related UFP may be assessed reasonably well by collecting on-site short-term traffic and land-use data.