Comprehensive Traffic Data Guide for Developers

Building a navigation or routing application with reliable traffic intelligence requires understanding what data sources exist, how they work, and how to combine them effectively. This guide covers everything from individual source characteristics to integration architecture for comprehensive coverage.

Part 1: Understanding Traffic Data Sources

Traffic data comes from five primary source categories. Each has distinct characteristics that determine its value for routing applications.

Source 1: 911/PSAP Dispatch Data

When someone calls 911, the Public Safety Answering Point (PSAP) creates a structured incident record with location, type, severity, and response units. This is the gold standard for verified incidents but has inherent latency.

Source 2: Telematics/Connected Vehicles

Fleet telematics and connected vehicle platforms detect incidents through speed anomalies, hard brake events, and GPS traces. Good for broad detection but cannot determine incident type or severity without visual confirmation.

Source 3: Traffic Camera AI

AI processing of traffic camera feeds provides the fastest reliable detection. Coverage is limited to camera locations (primarily urban highways) but detection includes rich context: lanes blocked, vehicles involved, emergency response presence.

Source 4: Dashcam AI

Fleet and consumer dashcams processed with edge AI extend visual coverage to roads without fixed cameras. Coverage follows traffic patterns rather than infrastructure.

Source 5: Roadway Sensors

Loop detectors, radar sensors, and infrastructure monitors provide ground-truth speed and volume measurements. Excellent for validating GPS-based estimates but cannot identify incident causes.

Part 2: Integration Architecture

Building comprehensive traffic intelligence requires thoughtful architecture that handles multiple sources with different characteristics.

Data Normalization

Define a canonical incident schema that captures:

interface Incident {
  id: string;
  type: 'accident' | 'congestion' | 'construction' |
        'hazard' | 'weather' | 'road_closure';
  severity: 'minor' | 'moderate' | 'major' | 'critical';
  location: {
    lat: number;
    lng: number;
    road: string;
    direction?: string;
    lanes_affected?: number;
  };
  detected_at: ISO8601;
  sources: Array<{
    type: 'camera' | 'dashcam' | 'telematics' |
          '911' | 'sensor';
    confidence: number;
    detected_at: ISO8601;
  }>;
  estimated_clearance?: ISO8601;
}

Deduplication Logic

The same incident may appear in multiple sources. Implement spatial-temporal clustering to merge duplicates while preserving source attribution:

• Events within 500m and 5 minutes are candidates for merge
• Visual sources (camera, dashcam) take precedence for incident details
• 911 data provides authoritative severity confirmation
• Aggregate all source detections for confidence scoring

Confidence Scoring

Not all detections are equally reliable. Implement confidence scoring that accounts for:

• Source reliability: Visual confirmation > telematics inference
• Multi-source corroboration: Detection by multiple sources increases confidence
• Temporal freshness: Recent detections are more reliable
• Model confidence: AI detection confidence scores

Latency-Aware Processing

Different sources have different latencies. Design your pipeline to:

• Publish fast detections (camera, dashcam) immediately
• Enrich with slower sources (911, sensor validation) as they arrive
• Update confidence scores as corroborating data appears
• Handle out-of-order arrivals gracefully

Part 3: Coverage Analysis

Understanding where your data coverage is strong or weak is essential for routing quality. Implement coverage analysis that:

• Maps source coverage geographically
• Identifies road segments with single-source dependency
• Tracks detection performance by source and region
• Monitors source reliability and uptime

Part 4: Build vs. Buy Decision

The final architectural decision is whether to build multi-source integration yourself or use an aggregated platform.

Build Yourself When:

• Traffic intelligence is a core competitive advantage
• You have specialized requirements not met by aggregated APIs
• You need direct relationships with data providers
• You have engineering capacity for ongoing maintenance

Use Aggregated APIs When:

• Time-to-market is important
• You want comprehensive coverage without integration complexity
• Traffic data is a feature, not your core product
• You prefer operational costs over engineering investment