Federated and reinforcement learning adapts roadside traffic signals to varied intersections while aggregating model parameters across servers.
Phase timing and turn-movement data create dequeue-zone and queue visuals, helping users compare intersection supply with demand.
Sensors detect vehicle speed and direction, while a curb-embedded light array gives entering drivers real-time roundabout safety cues.
Radio reflections identify stopped vehicles, traffic volume, and sudden crossing events to adapt pedestrian light control.
Using predicted vehicle movements, the server calculates group influence and controls traffic displays so higher-impact groups can continue traveling.
Sequentially extinguished LED sections give drivers advance warning of traffic signal changes, helping reduce confusion and accidents.
Centralized base-station monitoring detects a vulnerable road user leaving a cluster and sends warnings without shifting processing to mobile terminals.
Traditional sensors can miss intersection blind areas; feature data and signal timing support closed-flow accumulation for accurate road-section volumes.
A camera-based warning light monitor shares railroad crossing status with traffic controllers to reduce unnecessary vehicle-flow disruptions.