The freight system's complexity and significant impact on urban areas necessitate carefully considering sustainable transportation options. The proposed freight transit tour synthesis (FTTS) model, using fuzzy logic and entropy maximization, analyzes freight and transit systems as a multiclass category, exploring scenarios where buses and trucks share infrastructure. The experiments demonstrate that capacity and maximum cost significantly influence the solutions obtained using fuzzy parameters, with epsilon-values indicating the best solution. Results may vary depending on available data, highlighting the need to explore solutions for different capacity levels if exceeded. The impact of the maximum cost constraint on tour flows is significant, emphasizing the importance of considering cost in optimizing tour flows. The model's robustness is evident across various subjective value of time (SVT) scenarios. The application of the FTTS model offers a novel approach to estimating tour flows, incorporating traffic counts and fuzzy parameters for immediate, relevant results. The model's multiclass formulation accurately represents real-world traffic conditions, considering congestion in traffic assignments. Overall, the FTTS model holds promise for optimizing tour flows and shared infrastructure between freight and transit systems, aiding decision-makers in urban transportation planning and resource allocation, ultimately leading to improved traffic management and infrastructure usage efficiency.