This paper presents an improved entropy-based freight tour synthesis (FTS) using fuzzy logic (FL). The one approach used to develop freight tour models' formulation is EM, whose objective is to obtain the most probable freight (trucks) tour flows distribution in the network based on traffic counts. These models consider fixed parameters and constraints. The costs, traffic counts, and demand of trucks vary depending on human behavior, and they are not captured detailly in the models. Then, as the FL can be included in modelling that variability, it allows obtaining solutions where some or all the constraints do not entirely satisfy their expected value but are close to it due to the flexibility provided by this method to the model. Moreover, to develop the formulation in this research, the modelling approach used¿based on FL theory¿ is membership function, specifically triangular membership function, which is defined by three points corresponding to the vertices. This optimization probl