UAV traffic monitoringIntermediate
Prediction is Better than Detection: Traffic Congestion Control using Drones
This preprint introduces a Mesa-based multi-agent simulation where UAVs patrol road junctions to detect and predict traffic jams and trigger adaptive signal control. Across sweeps of fleet size, traffic level, and network size, detection and prediction rates plateau once drone count roughly matches junction count; prediction-triggered signal changes cut jam duration by up to 8 ticks versus up to 5 ticks for detection-triggered changes, roughly doubling benefit. It suggests onboard prediction rather than larger fleets is the higher-value path for aerial congestion management.