Autonomous Vehicle Traffic Flow Optimization Using AI and 5G Communication Networks

Real-time optimization of autonomous vehicle (AV) traffic flow using 5G communication networks and AI algorithms could overcome the limitations of static traffic enforcement, which uses outdated technologies to manage fluctuating traffic demands. It allows dynamic flow. Such commodities cannot adapt in real time because to their rigorous handling requirements, which disrupts traffic dynamics and creates construction delays and traffic congestion. The study suggests transmitting real-time vehicle status data to nearest intelligent crossings via vehicular wireless networks (VWNs). These intersections enhance traffic flow and reduce 5G network congestion using AI algorithms. Predictive analytics will help AVs change routes while on the road to maximize travel efficiency. It finds that peak congestion, average route duration, and fuel use reduce vehicle CO2 emissions by 66.7%, 22%, and 15%, respectively. It determined that the proposed system was a viable design route for a deployable smart city transportation solution because it outperformed traditional techniques in economic and environmental criteria.