Traffic congestion detection: Datasets & Models
A computer vision workflow that detects and counts vehicles on roadways to identify traffic congestion in real time from camera feeds. It classifies road segments as free-flowing or congested based on vehicle density and occupancy.
Traffic congestion detection
This detection workflow analyzes traffic camera imagery to locate and count vehicles such as cars, trucks, and buses, then estimates congestion levels based on vehicle density per lane or zone. Built on a traffic congestion dataset, it accepts inputs from fixed intersection cameras, highway CCTV, drone footage, or dashcam streams and outputs bounding boxes and per-segment congestion classifications.
Try it out
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Why automate traffic congestion detection
Manually monitoring dozens of traffic feeds is labor-intensive, inconsistent, and impossible to scale across a city's camera network. Automating congestion detection with computer vision gives traffic operations centers continuous, objective measurements without hiring more operators to watch screens. For smart cities and transportation agencies, this enables faster incident response, adaptive signal timing, and data-driven infrastructure planning. Detection models turn raw video into structured vehicle counts and congestion states that feed directly into traffic management systems.
Key benefits
Teams get consistent, around-the-clock congestion monitoring that integrates with existing traffic camera and CCTV infrastructure rather than requiring new hardware. The approach scales from a single intersection to an entire metropolitan network, and detection outputs can be aggregated into dashboards, alerts, or automated signal control. Starting from a labeled dataset and a strong detector like RF-DETR shortens the path from prototype to deployed system.
How to use it
- Upload an image: Drop any photo into the widget above to try traffic congestion detection for free, right on this page.
- Preview instant results: The model detects car, truck, bus, motorcycle, traffic light within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
- Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train RF-DETR on your own images for production accuracy.
- Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.
Recommended model for traffic congestion detection
RF-DETR is state of the art for real-time object detection, instance segmentation, and keypoint detection, with higher accuracy and lower latency than comparable models, and it's Apache 2.0 licensed. When you're ready to build a production model for traffic congestion detection, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Traffic Congestion Detection project to Roboflow Agent.