A Trilevel Transportation Problem for Traffic Management in Neutrosophic Environment

Abstract

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. 

DOI: 10.5281/zenodo.16340568