Leveraging SUMO for Traffic Twins: Experiences in Urban Traffic Processing
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The TORRES project develops an AI-driven framework that integrates diverse mobility data sources with SUMO simulations to improve real-time traffic monitoring, prediction, and decision-making in urban environments. Applied in Brussels and Namur, the framework focuses on three key contributions: traffic calibration, data interpolation, and simulation-assisted prediction for efficient mobility management.