Every day, millions of drivers rely on Google Maps and Waze for traffic updates. These apps have revolutionized navigation, but they share a fundamental limitation: they depend on humans to report incidents. By the time enough users report a crash, 10-15 minutes have often passed—and thousands of vehicles are already stuck.
The Crowd-Sourcing Problem
Apps like Waze and Google Maps use crowd-sourced data for incident detection. Here's how it works:
- An incident occurs on the highway
- Drivers in the area slow down or stop
- Some drivers manually report the incident in the app
- The app aggregates reports and GPS data showing slowed traffic
- After reaching a confidence threshold, the incident appears on maps
- Other drivers finally receive rerouting suggestions
This process takes time. Our analysis of incident detection across major metros shows crowd-sourced apps typically take 8-15 minutes to surface incidents after they occur. During rush hour in congested areas, this delay can extend to 20+ minutes.
The Argus Approach: Computer Vision at the Source
Argus takes a fundamentally different approach. Instead of waiting for humans to report incidents, we watch traffic cameras directly using computer vision.
How Argus Detection Works
- AI monitors camera feeds 24/7 – Our models watch thousands of traffic cameras simultaneously
- Incident detected in <10 seconds – Computer vision identifies crashes, stalled vehicles, debris, and abnormal slowdowns
- Alert dispatched immediately – Fleet managers, navigation apps, and drivers receive alerts
- Rerouting happens before congestion – Drivers avoid the incident entirely
Real-World Speed Comparison
We continuously benchmark our detection speed against major traffic data providers. Here's what we typically see:
| Provider | Detection Method | Typical Latency |
|---|---|---|
| Argus AI | Computer Vision | <10 seconds |
| Google Maps | Crowd-sourced + GPS | 8-15 minutes |
| Waze | Crowd-sourced | 5-12 minutes |
| INRIX | Probe data + aggregation | 3-8 minutes |
Why 15 Minutes Matters
A 15-minute head start on incident detection isn't just a nice-to-have—it's transformative for fleet operations and navigation apps.
For fleets: A truck entering a highway backup loses an average of 45 minutes. With early detection, dispatch can reroute before the driver ever hits traffic. Over thousands of deliveries, this adds up to millions in savings.
For rideshare drivers: Every minute stuck in avoidable traffic is money lost. Drivers using faster traffic intelligence complete more rides per shift.
For navigation apps: The app that reroutes users first wins their trust. Nobody wants to be the driver stuck in traffic while their friend with a different app cruised past on an alternate route.
The Technical Edge
Our computer vision models are trained specifically for traffic incident detection. They recognize:
- Vehicle collisions and their severity
- Stalled vehicles and debris on roadways
- Abnormal traffic patterns indicating incidents upstream
- Emergency vehicle presence and response staging
- Construction and lane closures
- Weather-related road hazards
By processing video feeds directly, we eliminate the human reporting delay entirely. The moment something happens on camera, we know about it.
Getting Started
Whether you're a fleet operator looking to reduce delays or a navigation platform seeking faster traffic data, Argus can help.
Ready to detect incidents faster?
See how sub-10-second detection can transform your operations.
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