Digital twin-enabled AI for sustainable traffic management: real-time urban mobility optimization in smart cities

An intelligent and agile traffic signal system has emerged as a vital sustainable component of urban mobility. The centralised traffic control systems currently in use are not capable of providing the required responsiveness or scalability to facilitate real-time traffic management. In this article, we propose a lightweight hybrid system that integrates a Gated Recurrent Unit (GRU) based predictive model and Digital Twin (DT) technology to provide decentralised, real-time traffic signal optimisation. The GRU model forecasts future localised congestion events from vehicle based Internet of Things (IoT) sensors, while the DT model ensures adequate performance by validating and adjusting control actions based on live information concerning roadway condition changes. We present results that demonstrate improvement of predictive accuracy by 33% (mean absolute error = 4.5), control latency of 78 ms, and a 15% decrease in CO 2 emissions, along with substantial decreases in both congestion and travel time. It was found that the proposed model consistently outperformed the state of the art solutions with improved prediction, latency, and environmental efficiency. The proposed architecture provides superior real-time traffic management within smart city environment.