Scalable Intelligent Traffic Balancing: Advancing Efficiency, Safety, and Sustainability in Urban Transportation Through Machine Learning and AIM Integration
With the growing demand for efficient, safe and sustainable transportation systems, the imperative to design intelligent routing and traffic management solutions within urban settings, requiring minimal data exchange and ensuring scalability, becomes evident. This paper introduces an innovative paradigm for traffic management. By seamlessly integrating machine learning and Autonomous Intersection