Solving Fragmented Traffic Data Through Multi-Source Aggregation

If you've ever built a navigation or routing application, you've encountered the traffic data problem: no single source provides complete coverage. Telematics misses visual context. Cameras have geographic gaps. 911 data has latency. The result is fragmented intelligence that leads to suboptimal routing decisions.

The Fragmentation Problem Defined

Traffic data fragmentation occurs when routing applications rely on incomplete data sources, leading to:

• Coverage gaps: Incidents in areas not covered by your data source go undetected
• Detection latency: Some sources take minutes to identify incidents
• Missing context: Knowing there's an incident without knowing severity or type
• Data silos: Valuable information locked in incompatible systems

Each single-source approach has fundamental limitations that cannot be solved by better algorithms—they require additional data inputs.

Understanding Each Data Source's Gaps

911/PSAP Dispatch Data

Strength: Human-verified incidents with emergency response context. High accuracy for confirmed accidents.

Gap: 2-5 minute detection latency from incident occurrence to dispatch. Doesn't capture incidents where no one calls 911 (minor fender benders, debris, congestion).

Telematics/Connected Vehicles

Strength: Broad geographic distribution. Good at detecting speed anomalies and hard brake events.

Gap: Only 3-5% of vehicles share telematics. No visual context—can't distinguish accident from normal congestion. Data locked in competing provider silos.

Fixed Traffic Cameras

Strength: Continuous monitoring of covered areas. Visual context enables incident classification. Sub-10-second detection latency.

Gap: Fixed infrastructure covers only 15-20% of highway miles, concentrated in urban areas. Rural roads and arterials largely uncovered.

Roadway Sensors

Strength: Accurate speed and volume measurements. Good for detecting congestion patterns.

Gap: Cannot identify incident cause. Limited geographic deployment. Maintenance issues create data gaps.

The Aggregation Solution

Multi-source aggregation solves fragmentation by combining complementary data sources. Each source fills the gaps left by others:

Technical Architecture for Aggregation

Building a multi-source traffic intelligence system requires solving several technical challenges:

1. Deduplication

The same incident may appear in multiple sources. A traffic camera detects an accident that telematics also flags through hard brakes, while 911 receives a call about it. Without deduplication, you'd report three incidents instead of one.

Deduplication requires spatial-temporal clustering: events within X meters and Y seconds are evaluated for merge. Confidence weighting prioritizes visual confirmation over telematics inference.

2. Normalization

Each source reports incidents differently. 911 data uses call codes. Telematics reports speed anomalies. Cameras classify by visual type. Aggregation requires normalizing to a common incident taxonomy.

3. Confidence Scoring

Not all detections are equally reliable. Visual confirmation from cameras carries higher confidence than telematics inference. Multi-source corroboration increases confidence. Each incident should carry a confidence score for downstream decision-making.

4. Latency Optimization

The fastest source wins for initial detection. Camera AI can detect in under 10 seconds. Waiting for 911 confirmation would add minutes. Aggregation systems should publish detections immediately while enriching with additional sources as they arrive.

API Integration Patterns

For data engineers building routing applications, aggregated traffic APIs should provide:

• Source attribution: Know where each incident detection originated
• Confidence scores: Weight routing decisions by data quality
• WebSocket streams: Real-time updates without polling
• Historical access: Pattern analysis for predictive routing