Inside ZIG AI.
How Verified Fare Evasion Detection Works.
ZIG AI connects what cameras detect with what the fare system actually recorded. Edge processing, AFC integration and journey intelligence work together so authorized teams receive a verified fare-compliance signal rather than a visual assumption.
ZIG AI is part of the ZIG Platform. It extends ZIG validation and real-time intelligence to fare compliance by combining boarding detection, fare-transaction verification, configurable journey rules and operational reporting.
Five Layers. One Verified Answer.
Edge AI fare verification
Processes time-sensitive validation close to the vehicle or station.
Computer vision + CCTV overlay
Detects boarding and provides authorized event context.
Open AFC integration
Confirms taps, payments and applicable fare records.
Journey intelligence
Applies transfers, fare caps, zones and concession rules.
Machine learning
Uses reviewed outcomes to improve deployment performance.
From Boarding to Verified Alert
A boarding event is detected
Edge checks available fare transaction data
Journey rules reduce avoidable false positives
Verified anomaly is routed per agency policy
Reviewed outcomes support model improvement
Edge-First by Design
Edge Processing (ZIG AI)
- Boarding detection
- Time-sensitive verification
- Local alert generation
- Continuity during network interruptions
Traditional Network Level
- Journey rules
- Fleet analytics
- Synchronization
- Model improvement
Verified Detection vs. Visual Inference
| Criteria | Camera-only systems | ZIG AI |
|---|---|---|
| Payment judgment | Inferred from visible rider behavior | Checked against AFC transaction records |
| Transfer rules | Not evaluated | Applied per agency configuration |
| Concessions & fare caps | Often misread as evasion | Recognized as valid fare conditions |
| False positives | Higher, requiring manual review | Reduced through verified data checks |
| Infrastructure | Replaces or duplicates AFC | Overlays existing AFC and validation |
What ZIG AI Connects To
CCTV & onboard cameras
Validators & fareboxes
Mobile ticketing & gates
AFC back-office
Operations dashboards
Enforcement devices
Machine Learning That Improves With Deployment
Continuous improvement
Model updates informed by reviewed field outcomes.
Fleet-wide propagation
Validated updates distributed across the deployment.
Coverage analysis
Camera coverage and placement assessed over time.
Environment calibration
Tuned to vehicle layouts, lighting and boarding patterns.
Non-Biometric by Design
- No facial recognition or biometric matching
- Behavior and fare status, not personal identity
- Local processing for time-sensitive video analysis
- Role-based access and audit controls
- Agency-configured retention and image handling
- No persistent cross-network movement tracking
Vehicle / Station (Local)
No biometric identifiers extracted or stored.
Controlled Event Output
Fare-compliance signal only • role-based access • audit logged
Built to Overlay, Not Replace
Works with current AFC and validation infrastructure
Supports bus, BRT, rail, streetcar and open-platform environments
Scales from a defined pilot to broader rollout
Provides hotspot, trend and operational-impact analytics
See the Architecture in Action.
Get a technical walkthrough of the detection pipeline, AFC integration, edge processing, journey rules and privacy controls with your agency infrastructure in mind.