Mathematical Model Using Intuitionistic Anti-Fuzzy–Fuzzy Combination Graph to Manage Road Traffic Networking.

Traffic management poses pervasive challenges in urban areas, resulting in substantial time and monetary losses, alongside critical safety risks. This paper presents a novel approach that merges graph theory with fuzzy logic to optimize the placement of traffic signals, signs and personalized traffic route selection. Emphasizing the enhancement of road infrastructure as a pivotal solution, the study integrates fuzzy graph theory to normalize parameters with varying limits. The innovative framework explores the connectivity of Intuitionistic Anti-Fuzzy–Fuzzy Combination Graphs (IA F FCG), unveiling an optimization strategy tailored to specified parameter preferences. This holistic approach offers a comprehensive solution to the complex challenges of traffic management and road infrastructure enhancement. Furthermore, the study introduces a method for normalizing traffic congestion using IA F FCG theory which enables the examination of the strength and frailty of network connectivity simultaneously, utilizing anti-fuzzy–fuzzy membership and non-membership functions that capture the uncertainty of traffic conditions across categories ranging from easy to difficult. Finally, the incorporation of fuzzy automata into this IA F FCG presents a groundbreaking approach in the field of decision-making, marking a significant advancement in this domain. [ABSTRACT FROM AUTHOR]