This paper presents an entropy-based transit tour synthesis (TTS) using fuzzy logic (FL) based on Entropy Maximization (EM). The objective is to obtain the most probable buses tour flows in the network based on traffic counts. These models consider fixed parameters and constraints. Costs, traffic counts, and demand of buses depending on different aspects, they are not captured detailly in models. As the FL can be included in modelling that variability, it allows obtaining solutions where some or all the constraints do not entirely satisfy expected value but are close to it due the flexibility provided by this method. This optimization was transformed into a bi-objective problem when optimization variables are the membership and the entropy. The performance of the proposed formulation was assessed in Sioux Falls Network. The results show that the FL inclusion with EM approach to estimate buses tours flows, applying synthesis method (traffic counts), improves quality of tour estimation