Siloed Traffic Data: Breaking Down Integration Barriers

Building a routing application with comprehensive traffic intelligence sounds straightforward: integrate traffic data sources and route around incidents. In practice, each data source exists in its own silo with different access methods, formats, latencies, and coverage areas. Breaking down these silos is one of the hardest problems in traffic data engineering.

Anatomy of Traffic Data Silos

Traffic data silos form naturally because each data source serves different primary purposes:

Telematics Provider Silos

Fleet telematics providers (Samsara, Geotab, Verizon Connect, etc.) collect vehicle data primarily for fleet management: tracking, compliance, driver safety. Traffic intelligence is a secondary use case, so data sharing APIs are often limited or expensive.

Worse, telematics providers compete with each other. Integrating with one gives you their fleet's coverage; comprehensive coverage requires contracts with multiple providers who may have overlapping but different fleet networks.

OEM Connected Vehicle Silos

Each automaker treats connected vehicle data as a competitive asset. GM's OnStar data doesn't flow to Ford. BMW's connected car data stays within BMW's ecosystem. Even within a single brand, data sharing policies vary by model year and trim level.

Government Agency Silos

State DOTs operate independently, each with different systems for traffic cameras, roadway sensors, and incident reporting. California's data format differs from Texas's differs from Florida's. There's no national standard for traffic data exchange.

Within states, local agencies often maintain separate systems. A metropolitan planning organization might have different data than the state DOT covering the same area.

Technical Challenges of Silo Integration

Format Inconsistency

Each silo uses different data formats. Some provide GeoJSON, others CSV dumps, others proprietary binary formats. Incident types have different taxonomies. Timestamps may be in different timezones or formats. Coordinates may use different projections.

Latency Variation

Real-time feeds have different definitions of "real-time." Some APIs update every 5 seconds, others every 5 minutes. Some push data, others require polling. Mixing sources with different latencies requires careful timestamp handling.

Coverage Overlap and Gaps

Different sources cover different road networks. Combining them requires understanding where coverage overlaps (causing duplicate detection) and where gaps exist (causing missed incidents). Road network matching is non-trivial.

Reliability and SLAs

Government data feeds often lack uptime SLAs. A DOT camera system going offline during a storm—exactly when you need it most—is common. Building redundancy across silos is essential but complex.

Strategies for Breaking Down Silos

1. Build vs. Buy the Integration Layer

The first decision is whether to integrate silos yourself or use a platform that has already done the work. Building integrations provides maximum control but requires significant ongoing maintenance as each source evolves.

2. Normalize Early

Convert each source to a common internal format as early as possible in your pipeline. Define a canonical incident schema that captures the superset of fields across sources, with source-specific attributes as metadata.

3. Build Source-Aware Logic

Each source has different reliability and latency characteristics. Your routing logic should understand these differences: a camera-detected incident with visual confirmation should be weighted differently than a telematics anomaly without corroboration.

4. Design for Partial Failure

Individual sources will fail. Design your system to degrade gracefully, falling back to available sources when others are unavailable. Monitor each integration independently.

The Aggregated API Approach

Rather than building and maintaining multiple integrations, routing applications can use aggregated traffic APIs that have already unified multiple sources. This approach provides:

• Single integration point: One API instead of dozens
• Normalized data: Consistent format across all sources
• Deduplication: Same incident from multiple sources appears once
• Source attribution: Know where each detection came from
• Managed reliability: Platform handles source failovers