The fuzzy optimization approach is known to be useful for multiobjective and multiconstraint decision situations in which the objectives and constraints are approximate. Although many transportation problems are in this category, the actual application of fuzzy optimization has been rare so far. This paper introduces the fuzzy optimization technique and tests this approach by applying it to setting the signal timing, in which the values of the parameters are not well defined. Three existing signal-timing models are reformulated into the fuzzy optimization approach to accommodate the uncertainty in the parameter values of the models. The results are compared with those obtained from the traditional formulation of the three models. The models tested are the Highway Capacity Manual approach, Webster's method, and Akcelik's method. For average delay and fuel consumption in general, the results obtained from the fuzzy optimization approach are found to be smaller than those obtained using the traditional approach of the three models. This study finds that the fuzzy optimization approach can be useful for transportation problems when objectives and constraints are many, when the boundary values of the constraints are not well defined, and when the goodness of the solution is based on acceptability or best compromise.