Intelligent Transportation Systems: A Review of Smart Infrastructure and Vehicle Technologies

Smart transportation systems and automated vehicles are revolutionizing urban mobility by integrating advanced technologies to enhance efficiency, safety, and sustainability. In order to set up routing efficiency, this model takes into consideration the physical state of the road and traffic density. This research synthesizes recent advancements and challenges in the field, drawing insights from interdisciplinary studies and practical implementations. Key technological breakthroughs include vehicle-to-everything (V2X) communication, AI-driven traffic management, and high-resolution data analytics, which optimize traffic flow and reduce congestion. Automated vehicles leverage digital twin techniques and sensor fusion to improve decision-making in complex environments, while Mobility-as-a-Service (MaaS) frameworks redefine urban travel patterns. These innovations are supported by infrastructure upgrades such as smart intersections and edge-cloud computing platforms, as seen in projects like the Ann Arbor Connected Vehicle Testing Environment. Reciprocal multi-instance convergence learning (RMICL) is used to pursue the routing and traffic avoidance decisions. Reciprocal multi-instance convergence learning (RMICL) is used to pursue the routing and traffic avoidance decisions. For smart cities, it takes into account better traffic avoidance and guaranteeing convergence robustness in dynamic traffic management. The conversation breaks down the fundamental elements of real-time traffic systems with an emphasis on the urgent need for quick, data-driven judgments in urban transportation. Real-time surveillance presents a number of ethical and practical challenges, including privacy concerns and technological obstacles. With a focus on communication protocols that support seamless connectivity, the work navigates the mutually beneficial interaction between AI and IoT.