Optimising Intelligent Transportation Networks Using Fuzzy Directed Graph

This paper presents a novel approach for optimising Intelligent Transportation Networks (ITN) using fuzzy digraph products combined with domination number and arborescence weight concepts. Uncertainty factors that may include traffic jams, variability in travel time, accidents, and variable road situations impact effective decision-making and performance within an intelligent transportation system. These uncertainties have been captured by representing the transportation network as a fuzzy directed graph, where nodes depict transportation centres, subway stations, or even junction points, and arcs are directional fuzzy relationships that represent traffic movement and strength. The fuzzy digraph modular product will be employed to effectively merge different transportation sub-networks for analysis. The use of the domination number will help in identifying the most effective points for control and surveillance. Moreover, the arborescence weight is employed to locate the least-cost spanning tree that connects all important nodes while minimising travel costs, traffic, and expenses. The incorporation of the domination number and arborescence weight in fuzzy digraph products serves as an effective approach to improving connectivity, avoiding redundancy, and increasing route reliability in unpredictable traffic situations. The developed approach facilitates intelligent traffic management decisions for the design of transportation systems in metro cities. The suggested approach helps develop scalable and sustainable transportation networks for future smart transportation applications.