Drones predicting traffic jams improve congestion control better than detection
Prediction is Better than Detection: Traffic Congestion Control using Drones
RoboticsMultiagent Systems
Summary
Traffic jams can be hard to manage quickly because it takes time to spot them first. The authors show that using drones to predict traffic jams before they fully happen helps traffic lights reduce jam duration twice as much as just detecting the jams. They also found that having enough drones to cover all intersections works well, but adding more drones beyond that doesn’t help much. Instead, making drones smarter at predicting traffic jams is a better way to improve traffic flow.
What this means in practice
- •For traffic management teams: Control traffic signals with drone-based jam predictions to reduce congestion duration more effectively than using jam detection alone.
- •For urban planning departments: Design drone fleet sizes to match the number of monitored intersections for efficient and sustained traffic monitoring without over-provisioning.
Authors
Samira Hayat, Christian Raffelsberger
Abstract
A central question in deploying teams of mobile robots for persistent monitoring is how task performance scales with fleet size, and whether this scaling holds once sensing drives downstream action rather than mere observation. We study this question for a team of drones performing traffic-jam detection and prediction in a simulated road network, whose reports drive an adaptive traffic-signal controller in closed loop. We build a multi-agent simulation, with vehicles following Nagel-Schreckenberg cellular-automaton dynamics and drones patrolling junctions via a round-robin policy, and sweep fleet size, traffic level, and network size to evaluate detection rate, detection delay, and prediction rate. We show how performance plateaus for fleet size approximating the number of junctions being monitored, and offer a general fleet-provisioning rule for persistent-monitoring deployments. More significantly, adapting the signal on a predicted jam, rather than a detected one, roughly doubles the resulting reduction in jam duration, showing that the value of onboard prediction in a sensing-to-action pipeline can exceed the value of adding more robots. Prediction accuracy, not sensing coverage, is now the binding constraint on further improvement, pointing to onboard inference, not fleet size, as the more promising direction for future work.