Although traffic and weather information systems indicate existing air and road surface temperatures, many drivers want to know not the temperature but the degree of road slipperiness. The friction coefficient is the best index for snow and ice conditions, but it is difficult to calculate. Some weather condition data are closely correlated with the friction coefficient. In this study, the use of weather and traffic data to determine the degree of road slipperiness was examined quantitatively by analyzing field data obtained at an intersection on a trunk line. Two kinds of filters were effective in extracting the slipperiness component from the observed data--the Kohonen Feature Map (KFM) and principal component analysis (PCA). KFM kept the distribution of the observed data uniform by eliminating excessive data, and PCA eliminated multicollinearity among the observed variables. The first two principal components successfully represented the original observed data with an accumulated proportion of more than 0.85. A linear multiple regression model, in which the PCA score values were adopted as the explanatory variable, was also justified. The multiple correlation coefficient was satisfactory for many patterns, and the radiation-related data were effective in improving estimate precision.