Traffic Analytics

How It Works

A traffic analytics pipeline combines several AI capabilities:

1. Vehicle detection — a neural network locates each vehicle per frame.
2. Classification — vehicle type is assigned (passenger car, truck, bus, motorcycle, bicycle).
3. Tracking — a multi-object tracker links detections across frames into trajectories.
4. Measurement — speed, direction, lane usage, and dwell are calculated from trajectories.
5. Event detection — stopped vehicles, wrong-way driving, pedestrians on roadway, and congestion.

Why It Matters

Traffic planners historically relied on induction loops in pavement — expensive to install, limited to counting, and blind to vehicle type. Video-based analytics deliver:

• Richer data — classification, speed, and path, not just counts.
• Flexible deployment — no road surface work needed.
• Incident detection — stopped vehicles and crashes flagged automatically.
• Retrospective analysis — archived video supports what-if studies.

IncoreSoft's Traffic Analytics module is integrated into Safe City deployments worldwide, feeding both operational dashboards and long-term planning tools.

Use Cases

• Adaptive signal timing — green-wave coordination based on live flow
• Congestion management — ramp metering, lane reversals, alerts
• Incident detection — stopped vehicles and secondary crashes
• Planning studies — origin-destination analysis via reidentification
• Commercial vehicle tracking — route and compliance analytics

Frequently Asked Questions

How accurate is video-based vehicle counting?

In well-placed daytime conditions, 95–99% accuracy is typical. Accuracy decreases at night (lower light) and in heavy precipitation; IR or thermal cameras help in these conditions.

Can it replace induction loops?

In most applications, yes. Loops remain standard where cameras cannot be mounted or for safety-critical signal triggering; for counting, classification, and planning, video is both cheaper and richer.

Is traffic analytics privacy-friendly?

Aggregate vehicle data (counts, speed, type) is not personal data. If license plates are captured and retained, privacy rules apply; many deployments anonymize plates automatically unless a specific incident requires retention.