A real-time adaptive traffic light control algorithm at urban intersections for smart cities

This paper investigates the challenges of intelligent traffic light control at urban intersections in the context of the Internet of Things. Increasing vehicle density and mobility have exacerbated traffic congestion and resulted in inefficient use of road infrastructure, particularly at intersections. In addition, the dynamic nature of traffic flow, the unpredictability of human driving behavior, and the complexity of network topologies pose significant obstacles to efficient traffic control. To address these issues, an intelligent control algorithm is proposed that exploits communication between vehicles and infrastructure, as well as between infrastructures. The algorithm incorporates an enhanced version of Dijkstra's algorithm to optimize traffic light operation and minimize vehicle waiting times by dynamically computing shortest paths based on real-time traffic data. Simulation experiments on an urban road network show that the proposed method significantly reduces delays and improves travel efficiency compared to traditional fixed-time traffic control systems. Performance evaluation based on metrics such as average vehicle delay and total travel time confirms significant improvements. In addition, the system demonstrates robustness under varying traffic loads and dynamic road conditions. Future research will focus on extending the approach to highway scenarios and integrating emergency vehicle prioritization mechanisms.