A Base Algorithm for Intelligent Traffic Management System for Urban Transportation using 6G Network

Efficient traffic management in urban environments is essential for minimizing congestion and ensuring smooth transportation flow. 6G network is expected to offer faster data speeds, lower latency, and enhanced connectivity. Traditional approaches often struggle to adapt to dynamic traffic patterns and overlook the influence of geographical factors on traffic flow. To address these challenges, this study proposes an innovative approach that integrates gravitational modeling and cluster detection techniques to optimize route planning and congestion management in transportation networks. The shorter response time of the algorithm makes it well-suited for traffic management in a 6G network. This capability allows for real-time data processing and immediate adjustments, enhancing overall traffic flow. The proposed approach utilizes gravitational modeling to simulate traffic interactions between road segments or intersections within the transportation network. By considering factors such as traffic density, user influence, and geographical proximity, the approach aims to identify clusters of interconnected road segments or intersections with similar traffic patterns. These clusters serve as strategic focal points for developing targeted route planning and congestion management strategies.