A Hybrid Digital Twin and AI Framework for Traffic Simulation and Route Finding

Abstract. Urban traffic congestion poses significant challenges for today's cities, affecting mobility, productivity, and environmental quality. The present study proposes a data-driven framework that integrates deep learning specifically Recurrent Neural Networks (RNNs) with Digital Twin (DT) technology to enhance travel time prediction and traffic management. The model utilizes real-time and historical data from sources such as Google Maps, weather services, and traffic sensors to capture temporal dynamics and external factors influencing traffic patterns. The RNN model exhibited a high degree of predictive accuracy, as evidenced by its R² value of approximately 0.94. Furthermore, its incorporation into a DT environment facilitated dynamic 3D simulations and route optimization. A comparative analysis revealed that the DT system exhibited a marked superiority over conventional navigation tools in congested scenarios, with a travel time reduction of up to 26%. The findings indicate the potential for a synergistic integration of artificial intelligence (AI) and data technology (DT) to facilitate the development of intelligent, adaptable urban transportation systems.