Route optimization depends on accurate, timely traffic data. But with multiple data sources available—GPS probes, cameras, telematics, 911 dispatch—how do you know which provides the best results? This analysis compares sources across the metrics that matter for routing.
What Makes Traffic Data "Best" for Routing?
Route optimization has specific requirements that differ from general traffic monitoring. The "best" data source is the one that:
- Detects incidents fastest: Earlier detection = more routing options
- Covers your road network: Gaps mean missed incidents
- Provides accurate context: Severity affects routing decisions
- Maintains reliability: Downtime means degraded routing
- Minimizes false positives: Bad data causes bad routes
Source Comparison: Detection Latency
Detection latency—the time from incident occurrence to data availability—is critical for routing. Faster detection means more time to calculate and communicate alternate routes.
Latency Comparison
Winner: Camera/Dashcam AI — Visual AI detection provides the fastest reliable incident detection, crucial for real-time rerouting scenarios.
Source Comparison: Coverage
Coverage determines where you can detect incidents. The best latency is worthless if the source doesn't cover your users' routes.
Coverage by Source
| Source | Urban | Suburban | Rural |
|---|---|---|---|
| GPS Probes | High | Medium | Low |
| Traffic Cameras | High | Low | Minimal |
| Dashcams | Medium | High | Medium |
| Telematics | Medium | Medium | Medium |
| 911/PSAP | High | High | High |
Winner: 911/PSAP + Dashcams — 911 provides universal coverage for serious incidents. Dashcams fill gaps with mobile visual coverage, especially on commercial routes that cameras don't reach.
Source Comparison: Context Quality
Context determines routing impact. Knowing an accident occurred isn't enough—you need to know severity, lanes blocked, and estimated duration.
- Camera/Dashcam AI: Excellent. Visual analysis shows lanes blocked, vehicles involved, emergency presence.
- 911/PSAP: Good. Human reports include severity and response dispatch.
- Telematics: Limited. Speed anomalies detected but cause unknown.
- GPS Probes: Minimal. Only indicates congestion, not incident type.
- Crowdsourced: Variable. Depends on report quality.
Winner: Camera/Dashcam AI — Visual context enables accurate severity assessment and routing impact estimation.
The Optimal Combination
No single source wins across all metrics. Optimal route optimization requires combining sources strategically:
Recommended Source Stack
- Primary detection: Camera + Dashcam AI
Fastest detection with visual context for severity assessment - Broad coverage: Telematics aggregation
Fills geographic gaps where visual sources are sparse - Verification: 911/PSAP integration
Confirms serious incidents, provides emergency response context - Validation: Roadway sensors
Ground-truth speed measurements for congestion assessment
Implementation Considerations
Source Weighting
Different sources should influence routing decisions differently based on their characteristics:
- Visual confirmation: High confidence, immediate routing impact
- Telematics anomaly: Medium confidence, investigate before major reroute
- 911 verified: Highest confidence for severity assessment
- Multi-source corroboration: Increases confidence for any source
Graceful Degradation
When primary sources are unavailable (camera down, dashcam coverage sparse), fall back to secondary sources rather than operating blind. Design your system to work with whatever data is available.
Key Takeaway
The "best" traffic data for route optimization isn't a single source—it's a strategic combination. Camera and dashcam AI provide the fastest detection with visual context. Telematics and 911 extend coverage. Sensors validate measurements. The optimal stack combines all of these, weighted by reliability and corroborated across sources.
Published by
Argus AI Team