This paper presents an analysis of the results achieved using fuzzy logic to model complex traffic and transportation processes. For solving a large number of different traffic and transportation problems, this is what we actually do: map a crisp input vector into a crisp scalar output. Fuzzy logic could be used successfully to model situations in which people make decisions in an environment that is so complex that it is very hard to develop a mathematical model. Present experience shows that there is room for the development of different approximate reasoning algorithms when solving complex problems of this type. Until a few years ago the `trial and error' procedure was the customary method used to develop fuzzy logic systems, and so researchers designed fuzzy logic systems independently of numerical training data.