An algorithmic approach to minimize road accidents in the highway system using Hamiltonian fuzzy influence graphs

Bottlenecks in interconnection networks cause delays, traffic jams and hinder traffic flow efficiency. Fuzzy influence graphs offer a solution by enabling multiple high-capacity pathways. These graphs are well-organized, practical, applicable, and effective for handling ambiguity in real-world problems involving fuzzy data and information. They could provide knowledge regarding the influence of a vertex on a vertex or an edge of the same or another graph, whether they are connected or unconnected. This research introduces domination in Hamiltonian fuzzy influence graphs (HFIGs), expanding fuzzy graph theory. We compute key parameters using strong fuzzy influence pairs and propose an algorithm for the minimum domination number, applicable to artificial intelligence. A highway accident minimization model using HFIGs is analyzed using the TOPSIS, VIKOR and EDAS methods.