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The innovative framework explores the connectivity of Intuitionistic Anti-Fuzzy\u2013Fuzzy Combination Graphs (IA[Formula: see text]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[Formula: see text]FCG theory which enables the examination of the strength and frailty of network connectivity simultaneously, utilizing anti-fuzzy\u2013fuzzy 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[Formula: see text]FCG presents a groundbreaking approach in the field of decision-making, marking a significant advancement in this domain.<\/jats:p>","DOI":"10.1142\/s1793005726500079","type":"journal-article","created":{"date-parts":[[2024,6,29]],"date-time":"2024-06-29T23:29:02Z","timestamp":1719703742000},"page":"111-128","source":"Crossref","is-referenced-by-count":0,"title":["Mathematical Model Using Intuitionistic Anti-Fuzzy\u2013Fuzzy Combination Graph to Manage Road Traffic Networking"],"prefix":"10.1142","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9374-0488","authenticated-orcid":false,"given":"C. 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