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 ε-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.

货运系统的复杂性和对城市地区的重大影响需要仔细考虑可持续的运输选择。所提出的货运运输旅游综合（FTTS）模型使用模糊逻辑和熵最大化，将货运和运输系统作为多类类别进行分析，探索公共汽车和卡车共享基础设施的场景。实验表明，容量和最大成本显着影响使用模糊参数获得的解决方案，其中 ε 值表示最佳解决方案。结果可能会根据可用数据而有所不同，这突出表明如果超出容量水平，则需要探索不同容量水平的解决方案。最大成本约束对旅游流的影响是显着的，强调了在优化旅游流时考虑成本的重要性。该模型的稳健性在各种主观时间价值 (SVT) 场景中都很明显。 FTTS 模型的应用提供了一种估计旅游流量的新颖方法，将交通计数和模糊参数结合起来以获得即时、相关的结果。该模型的多类公式准确地表示了现实世界的交通状况，并考虑了交通分配中的拥堵情况。总体而言，FTTS 模型有望优化旅游流量以及货运和交通系统之间的共享基础设施，帮助决策者进行城市交通规划和资源分配，最终改善交通管理和基础设施使用效率。