Many northern countries use a road weather information system (RWIS) with a network of stations to monitor winter road conditions. Present station locations were selected after field investigations of micro- and local-climate conditions (e.g. using thermal mapping). This paper describes an approach to optimally locate and equip the stations in order to best identify conditions hazardous to road transport. This is achieved using multiple regression analysis of observed data and correlation with location meta-data. A geographical information system (GIS) is used to develop quantitative and objective descriptions of station locations by using knowledge of local and regional climate variations. Road climate is described using a slipperiness classification, in which weather situations are classified into ten types of slipperiness from the meteorological variables collected at RWIS stations. The relationships between quantified locations and data on road slipperiness in southern Sweden during one winter are analysed. The results show that the spatial patterns for different types of slipperiness are significantly related to local parameters. The three most prevalent types are analysed in detail: snowfall on a frozen road surface, hoarfrost and low visibility, and strong formation of hoarfrost.