If you're building applications that need traffic intelligence—routing engines, ETA services, fleet management systems, traffic analytics—you've probably discovered that no single data source tells the complete story. Each has coverage gaps, latency limitations, and types of incidents it misses entirely.
This guide breaks down the five primary sources of real-time traffic data, what each does well, where each falls short, and why the combination matters more than any individual source.
The Five Sources
1. 911/PSAP Dispatch Data
When someone calls 911 to report an accident, that information enters the Public Safety Answering Point (PSAP) system. This data represents ground-truth, human-verified incidents with emergency response context.
Strengths
- Human-verified accuracy
- Emergency response ETA
- Incident classification
- Clearance updates
Limitations
- Only reported incidents
- Reporting delays (30-120s)
- No minor incident coverage
- Jurisdictional fragmentation
2. Telematics/Connected Vehicles
Connected vehicles and fleet telematics devices report speed, location, and events like hard braking. This probe data provides insights into traffic flow and can indicate incidents through anomaly detection.
Strengths
- Geographic coverage anywhere vehicles go
- Real-time speed and flow
- Hard braking/acceleration events
- Historical pattern data
Limitations
- Only 3-5% market penetration
- No visual context
- Siloed between providers
- Requires multiple probes to confirm
3. Roadway Sensors
Loop detectors, radar sensors, and other infrastructure provide continuous monitoring at fixed locations. These systems measure volume, speed, and occupancy with high accuracy where deployed.
Strengths
- Continuous 24/7 monitoring
- High accuracy at location
- Volume counts
- Queue detection
Limitations
- Fixed locations only
- Maintenance dependent
- No incident classification
- Aggregation delays (30-60s)
4. Traffic Camera Video Inference
AI-powered analysis of traffic camera feeds enables visual incident detection and classification. Computer vision can identify accidents, debris, stalled vehicles, and assess severity in real-time.
Strengths
- Visual context and classification
- Sub-10-second detection
- Severity assessment
- Lane-level precision
Limitations
- Fixed camera locations
- Weather/lighting sensitivity
- Camera network access required
- Processing infrastructure
5. Dashcam Video Inference
AI analysis of fleet and consumer dashcam footage extends visual coverage across the entire road network. Mobile cameras capture incidents, road conditions, and infrastructure issues from the driver's perspective.
Strengths
- Coverage everywhere vehicles go
- Driver-level perspective
- Road condition observations
- Infrastructure damage detection
Limitations
- Variable video quality
- Processing at scale challenges
- Privacy considerations
- Depends on vehicle presence
Why You Need All Five
Each source fills gaps the others leave:
- 911 data verifies major incidents that sensors might detect as anomalies
- Telematics covers roads where cameras and sensors don't exist
- Sensors provide continuous baseline data at key locations
- Camera AI adds visual context that telematics lacks
- Dashcam AI extends visual coverage beyond fixed camera networks
The Coverage Math
An incident visible to a traffic camera is detected in under 10 seconds. The same incident might take 30-60 seconds via telematics (waiting for enough vehicles to report), 60-120 seconds via 911 (human reporting and dispatch entry), or never be detected by sensors (if between detector locations). Multi-source fusion catches incidents faster and more completely than any single source.
The Aggregation Challenge
Combining five data sources isn't as simple as concatenating feeds. Key challenges include:
- Deduplication: The same incident may be detected by multiple sources—it needs to be recognized as one event
- Normalization: Each source has different schemas, confidence levels, and data formats
- Latency management: Faster sources shouldn't wait for slower ones
- Confidence scoring: Multi-source confirmation increases reliability
For Developers
If you're building applications that need traffic intelligence, the choice isn't which source to use—it's whether to build multi-source aggregation yourself or use a platform that handles it. Each source requires separate integrations, data processing, and ongoing maintenance. Aggregated APIs abstract this complexity, providing normalized, deduplicated traffic intelligence through a single integration.
Published by
Argus AI Team