A Trilevel Transportation Problem for Traffic Management in Neutrosophic Environment

Traffic congestion in urban areas presents a complex challenge due to the multi-level nature of transportation networks and the presence of uncertain, imprecise data. Existing models often fail to comprehensively address hierarchical decision-making under uncertainty. This paper fills this gap by proposing a novel interval neutrosophic goal programming model for the Trilevel Transportation Problem (TTP), which considers decision-making across three interconnected levels: highways, traffic hubs, and city streets. The proposed framework incorporates interval neutrosophic numbers to effectively represent uncertainty and indeterminacy in traffic data. We develop a structured goal programming approach to optimize traffic flow, minimize congestion, and improve travel efficiency. A detailed numerical example illustrates the applicability and superiority of the model compared to conventional methods. The results demonstrate that our model provides a more flexible and accurate solution for traffic planning under uncertain environments, offering valuable insights for urban transportation management and policy formulation.