This study proposes an AI-powered digital twin (DT) platform designed to support real-time traffic risk prediction, decision-making, and sustainable mobility in smart cities. The system integrates multi-source data—including static infrastructure maps, historical traffic records, telematics data, and camera feeds—into a unified cyber–physical platform. AI models are employed for data fusion, anomaly detection, and predictive analytics. In particular, the platform incorporates telematics–video fusion for enhanced trajectory accuracy and LiDAR–camera fusion for high-definition work-zone mapping. These capabilities support dynamic safety heatmaps, congestion forecasts, and scenario-based decision support. A pilot deployment on Madison’s Flex Lane corridor demonstrates real-time data processing, traffic incident reconstruction, crash-risk forecasting, and eco-driving control using a validated Vehicle-in-the-Loop setup. The modular API design enables integration with existing Advanced Traffic Management Systems (ATMSs) and supports scalable implementation. By combining predictive analytics with real-world deployment, this research offers a practical approach to improving urban traffic safety, resilience, and sustainability.

Microscopic traffic simulation models are promising for modeling detailed traffic features from the individual vehicle level, but they present challenges in optimization due to their computationally time-consuming, and non-analytic nature. This study addresses the difficulty of incorporating microscopic simulations into the optimization framework for multiple types of Active Traffic Management (ATM) strategies design by adopting the Simulation-Based Optimization (SBO) paradigm. A novel discrete SBO algorithm for highway ATM strategies designs, embedded within a Cell Transmission Model (CTM)-based meta model and a problem-specific Adaptive Hyperbox Algorithm (AHA) is proposed. The effectiveness of the proposed SBO method is validated through two cases: a toy highway and a real-world highway. The method is benchmarked versus the commonly utilized general-purpose model based-SBO algorithm. The experiments indicate that the proposed problem specific SBO algorithm outperforms the general-purpose model-based algorithms to obtain promising solutions in ATM strategies design problems, especially when the simulation resources are tight. Under ATM strategies derived by the proposed algorithm, the observed total delay on the highway shows a substantial 70% decrease. The ATM strategy obtained through the microscopic model-based optimization is proven to be superior to the macroscopic model-based approach. The introduced SBO algorithm in this work advances the current SBO theoretical framework by enhancing its applicability to highway traffic issues and overcoming obstacles related to the optimization of discrete variables.