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 Management (AIM), we propose a highly scalable routing and traffic balancing system. Our innovation significantly reduces data exchange requirements while optimizing traffic flow. By leveraging the power of machine learning algorithms, our proposed system aims to elevate the efficiency, safety, and sustainability of urban transportation networks. This paper provides an insightful overview of our AIM method, explores the application of machine learning in routing, and delineates our approach to achieve effective traffic balancing. Through extensive experimental results and evaluations, we demonstrate the efficacy of our proposed system in enhancing traffic flow and alleviating congestion in urban scenarios.