Roadway Data is Broken. We're Fixing It.
Traffic data is fragmented across dozens of siloed sources. DOTs don't share. Telematics providers compete instead of collaborate. Connected vehicles cover only a fraction of traffic. The result? Blind spots everywhere.
Argus AI is building the unified traffic intelligence layer the industry needs.
The Problem: Fragmented Roadway Data
Every organization managing traffic—from DOTs to fleet operators to ride-share platforms—struggles with the same fundamental problem: incomplete, delayed, and siloed data.
DOTs Operate in Silos
Each state DOT maintains separate systems. Data sharing between states is minimal, creating gaps at borders and along major corridors.
Telematics Providers Don't Share
Fleet telematics companies treat data as competitive advantage. Even combined, they cover only a fraction of total traffic.
Connected Vehicles Are Overhyped
Despite industry promises, only 3-5% of vehicles on the road have connected telematics. The 95%+ majority remain invisible.
30-60 Second Delays
Traditional traffic data sources have latency measured in minutes, not seconds. By the time you know about an incident, congestion has already formed.
Why Connected Vehicles Won't Save Us
The automotive industry has promised "connected vehicle" solutions for years. Here's why they fall short of solving the traffic intelligence problem.
Low Market Penetration
Only 3-5% of vehicles have connected telematics today. Full penetration is decades away.
Telematics Only
Connected vehicles report speed and location. They can't identify debris, pedestrians, or incident severity.
Data Silos
OEMs and telematics providers don't share data. GM data doesn't talk to Ford data doesn't talk to fleet data.
Privacy Restrictions
Consumer privacy concerns limit what data can be collected and shared, reducing utility.
No Visual Context
A hard brake event looks the same whether caused by traffic, an accident, or a pedestrian. Context is missing.
The Math Doesn't Work
Even at 5% connected vehicle penetration, you're blind to 95% of traffic. A major accident could happen with no connected vehicles involved. A debris incident won't trigger any telematics event. Waiting for full connected vehicle penetration means waiting decades—and still missing the visual context that only cameras can provide.
The Argus AI Approach
Instead of waiting for the connected vehicle future, we're building comprehensive traffic intelligence today by aggregating every available data source.
Aggregate Everything
We integrate 911 dispatch, telematics (all providers), roadway sensors, traffic cameras, and dashcam feeds into one unified pipeline.
AI Video Inference
Computer vision on traffic cameras and dashcams provides visual context that telematics cannot: incident type, severity, lanes blocked, and more.
Sub-10-Second Latency
Our low-latency data pipeline delivers alerts before congestion forms, enabling proactive routing instead of reactive.
One Normalized API
Regardless of source, all data is normalized into a single schema. One integration, complete traffic intelligence.
Building the Data Rails for Traffic Intelligence
We envision a future where traffic data flows as seamlessly as financial transactions. Just as payment rails connect banks, merchants, and consumers, Argus AI is building the data rails that connect traffic data producers to the applications that need it.
The Low-Latency Pipeline
- Real-time ingestion from 911 centers, telematics APIs, sensor networks, and video feeds
- AI-powered normalization that transforms disparate formats into unified, actionable intelligence
- Sub-10-second delivery to routing engines, fleet management systems, and traffic management centers
- Video inference context that tells you not just that something happened, but what happened and how severe it is
Traditional vs. Argus AI Approach
| Capability | Traditional Sources | Argus AI |
|---|---|---|
| Detection Latency | 30-60+ seconds | <10 seconds |
| Vehicle Coverage | 3-5% (connected only) | All vehicles (via video) |
| Visual Context | None | Full AI inference |
| Data Sources | Single provider silos | 6+ aggregated sources |
| 911 Integration | Rare | Native |
| Incident Severity | Estimated from speed | Visual confirmation |
Frequently Asked Questions
Why is roadway data so fragmented?
Roadway data fragmentation stems from how transportation infrastructure evolved. Each state DOT built independent systems. Telematics providers emerged as competitors, not collaborators. The automotive industry prioritized proprietary connected vehicle data. No organization had incentive to create a unified layer—until now.
What are the limitations of connected vehicle data?
Connected vehicles face several fundamental limitations: only 3-5% market penetration today, telematics-only data without visual context, siloed data between manufacturers and providers, and privacy restrictions on data collection. These limitations mean connected vehicles alone cannot provide comprehensive traffic intelligence.
How does video inference improve traffic detection?
Video inference from traffic cameras and dashcams provides visual context that telematics cannot. AI can identify incident type (accident vs. debris vs. weather), estimate severity, count lanes blocked, detect pedestrians or stopped vehicles, and confirm clearance. This context enables more accurate routing and faster response.
What makes Argus AI different from other traffic data providers?
Argus AI is the only platform that aggregates ALL major traffic data source types: 911 dispatch, multiple telematics providers, public sensors, traffic camera AI, and dashcam inference. This multi-source approach combined with sub-10-second latency provides coverage and speed that single-source providers cannot match.
Join Us in Building Better Traffic Intelligence
Whether you're a data provider, a platform builder, or an organization that needs better traffic data, we want to talk.