Raleigh’s traffic monitoring pilot builds on a partnership with Esri, NVIDIA, and Microsoft to deploy a digital twin of the city’s intersections and road networks. The system ingests video feeds from hundreds of real-time cameras to identify vehicles, cyclists, and pedestrians using computer-vision models from NVIDIA, says AEC Magazine.
Once processed, the data is visualized in Esri’s ArcGIS platform, where intersections are color-coded (green, yellow, red) according to congestion levels. The digital platform also flags incidents such as stalled vehicles affecting traffic flows, enabling rapid response by city staff.
The pilot’s purpose is threefold: (1) to give city planners insight into historic and current mobility patterns; (2) to optimize signal-timing and intersection layouts based on real-world usage; and (3) to enhance safety for vehicles, cyclists, and pedestrians by identifying dangerous intersections.
From an engineering and infrastructure perspective, the project marks a shift from passive data capture to active traffic management based on automated analytics. The integration of vision AI, geospatial models, and dashboards means the city can move from reactive (viewing crashes or congestion after the fact) to proactive (modifying intersections or signal logic ahead of problems).
The Raleigh case underscores several key takeaways: multimodal sensing (cars, bikes, pedestrians) is now feasible; digital twin platforms are extending beyond building interiors into full-city networks; and cross-vendor collaboration (hardware, software, cloud) is vital. The challenge ahead will be scaling the system, ensuring privacy and data governance, and linking insights into budgeted infrastructure changes rather than just dashboards.
Raleigh’s pilot is a concrete example of how AI-powered digital twins are moving from concept into city-scale mobility operations.