Digital Twin-Based Predictive Analytics for Urban Traffic Optimization and Smart Infrastructure Management

Abstract

In modern cities, urban traffic congestion remains a persistent issue that causes longer journey times, excessive fuel consumption, and environmental pollution. Traditional traffic management systems often employ static models that are insensitive to dynamic changes in urban mobility patterns in real time, which results in inefficient congestion relief. This study proposes a predictive analytics system based on digital twins to enhance smart city infrastructure management and optimize traffic flow to transcend these limitations. A Convolutional Neural Network–Gated Recurrent Unit (CNN-GRU) model is embedded at the core of the proposed system to effectively capture and learn spatial and temporal traffic patterns efficiently to enhance prediction accuracy and real-time decision-making. The scalability and robustness of the model are trained on actual urban traffic data. The system is developed and verified with Python, TensorFlow, and simulation-based digital twin platforms. The prediction capability of traffic conditions and congestion relief of the model is evidenced from the experimental results, which present a high prediction accuracy of 94.5%. Enhanced route planning, anticipatory congestion avoidance, and smart traffic signal control are some of the primary benefits. The outcome is that urban mobility has been enhanced and congestion in traffic has reduced substantially. This research contributes to the evolution of intelligent transportation systems by being the first to integrate deep learning-based predictive analytics with digital twin technology. Ultimately, the proposed framework encourages the emergence of future-oriented smart city infrastructure and the aim of sustainable city transport.