This paper presents an analysis of the results achieved using fuzzy logic to model complex traffic and transportation processes. Problem solving a large number of different traffic and transportation, this is what we actually do: map a crisp input vector into a crisp scalar output. The concept of a fuzzy logic system (FLS) or concept called ‘Approximate Reasoning’ as follows: ‘In general a FLS is a nonlinear mapping of an input data (feature) vector into a scalar output (the vector output case decomposes into a collection of independent multi-input/ single-output systems). It showed that vague logical statements enable the formation of algorithms that can use vague data to derive vague inferences. The goal of this paper was to classify and analyze results in the application of fuzzy logic when modeling complex traffic and transportation processes. 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. The results obtained show that fuzzy set theory and fuzzy logic present a promising mathematical approach to model complex traffic and transportation processes that are characterized by subjectivity, ambiguity, uncertainty and imprecision. As already noted, the benefits from the fuzzy logic will be more accurately assessed as the number of successful practical applications of the fuzzy logic in traffic control and transportation planning increases.