A leading UK education provider needed a scalable platform to unify institutional data and streamline analytics across educational systems. Aeologic developed a Docker-based solution that containerized applications, integrated student information systems, and enabled consistent deployments, improving operational efficiency, accelerating insights, and supporting scalable digital learning initiatives.
The authority operated 140 signalized intersections across three states with legacy inductive-loop sensors and siloed camera feeds. Traffic engineers adjusted signal timing manually based on historical patterns, and incident response relied on phone reports from field staff — often 12–18 minutes after an incident began.
Rather than replace existing sensor hardware, Aeologic layered a Sense → Decide → Act pipeline on top of it: normalizing feeds from inductive loops and traffic cameras, training SageMaker forecasting models on 14 months of historical flow data, and pushing predicted congestion windows back to the signal controllers as adaptive timing recommendations.
A GIS-based operations map gives traffic engineers a single live view of every intersection, with automatic anomaly flags and one-click rerouting suggestions during incidents.
"We went from finding out about congestion after the fact to seeing it forming twenty minutes before it happens. That's the difference between managing traffic and predicting it."
— Program Director, State Transport Operations (illustrative quote — replace with a verified client attribution)