A Predictive Digital Twin Framework for Real-Time Urban Traffic Management

Urban traffic congestion remains a critical challenge to mobility efficiency and environmental sustainability. This article presents a digital twin (DT) framework for real-time urban traffic management in Issy-les-Moulineaux, France. The framework integrates heterogeneous traffic data sources, including historical, real-time, and predictive data, into a unified architecture for scenario-based analytics and decision support. For traffic forecasting, deep learning models, including long short-term memory (LSTM) and Transformer-based approaches, are evaluated alongside baseline models such as Seasonal AutoRegressive Integrated Moving Average (SARIMA) and Neural Prophet. The LSTM model is selected based on its stable and consistent performance across heterogeneous traffic conditions, achieving mean absolute error (MAE) values ranging from approximately 2 to 33 vehicles per hour (veh/h) across different road segments. The system also supports dynamic event handling, including full and time-based road closures, with capacity-constrained and equal-allocation diversion strategies for first-order impact estimation. Quantitative evaluations demonstrate that the framework enables proactive congestion mitigation, comparative emission impact assessment, and improved interoperability with city platforms through next-generation service interfaces-linked data (NGSI-LD) standardization. By combining predictive modeling, real-time monitoring, and scenario-driven analytics, the proposed DT delivers actionable insights for urban planners, reducing short-term disruptions and supporting long-term sustainable mobility management.