A computer vision and multi-object tracking system that analyzes live traffic flow to drive adaptive, data-driven signal timing.
Conventional traffic signal systems operate on fixed timing cycles that do not adapt to real-time demand, producing dry waits at empty lanes and excessive congestion on high-load approaches.
MoreYeahs built an AI-powered traffic flow analysis and adaptive signal control system for city traffic authorities and smart city operations teams, replacing static timing plans with live, data-driven signal control.
Fixed-cycle signal control created avoidable delay and inefficiency at intersections:
Fixed-Cycle Inefficiency: signals did not adapt to real-time traffic demand, causing vehicles to wait at red lights despite empty lanes.
Rising Congestion Costs: inefficient timing increased travel time, fuel consumption, and emissions at busy intersections.
Manual Monitoring Doesn't Scale: human-adjusted signal timing cannot scale across busy intersections or respond continuously.
No Live Flow Visibility: traffic authorities lacked real-time lane-level data to inform timing decisions.
MoreYeahs built a camera-based traffic flow analysis system that uses deep learning and multi-object tracking to measure live traffic conditions and adapt signal timing accordingly.
Vehicle Detection and Tracking: deep learning models detect and classify vehicles, with multi-object tracking measuring count, lane density, queue length, and speed.
Live Traffic Metrics: the system computes real-time metrics per lane and approach.
Adaptive Signal Timing: a control module dynamically adjusts green time allocation based on live congestion levels.
Traffic Dashboard: live metrics and phase plans are visualized for traffic operators.
The pipeline runs continuously from live video ingestion through congestion scoring and signal timing recommendations.
Continuous Vehicle Tracking: cameras stream live intersection video, and a tracker assigns persistent IDs to detected vehicles by lane.
Congestion Scoring: vehicle counts, queue length, and speed feed a congestion score computed per approach.
Adaptive Timing Engine: optimal green splits are computed and sent to the signal controller or simulator.
Resilience Handling: heavy occlusion during peak load switches to density-based estimation, and camera feed loss falls back to a default timing plan.
The solution runs on fixed-mount intersection cameras paired with detection, tracking, and adaptive control logic.
In validation testing, the system met its target benchmarks for detection accuracy and delay reduction.
Data-driven signal control gives traffic authorities a scalable way to cut delay without rebuilding intersection infrastructure.
Reduced Congestion and Delay: adaptive timing measurably cuts idle wait time on low-traffic approaches.
Lower Fuel Use and Emissions: smoother traffic flow reduces unnecessary idling at intersections.
Continuous, Real-Time Responsiveness: signal timing adjusts to live conditions rather than fixed daily schedules.
Foundation for Smart City Scale: the same detection and tracking pipeline extends to additional intersections and city-wide traffic programs.
Tell us where you're headed. You'll get a senior architect on the first call, a working consultation, not a sales pitch.
