Instant traffic volume data for every road in your network
Multi-year historical trends and local model calibration to fine-tune accuracy for your region.
Traditional traffic counting was never designed to cover an entire network
Physical counters deliver trusted data, but only where they're deployed, leaving most of the network uncounted.
High cost per location
Deploying, maintaining and retrieving physical counters requires staff time, equipment and repeat site visits, multiplied across every location you need to cover.
Limited network coverage
Even well-funded count programs typically cover only a fraction of the road network, often leaving other priority roads unmonitored.
Slow data turnaround
Manual collection programs operate on seasonal or annual cycles. By the time data is processed and available, months may have passed and conditions may have changed.
Probe-data based volume estimation: Network-wide coverage without physical infrastructure
SMATS iNode starts with a sample of anonymized trips from probe data, then uses machine learning algorithms to expand that sample into full traffic volume estimates, calibrated against your ground-truth counts.
Every road, Every functional class
Volume estimates across interstates, arterials, collectors and local roads, covering your full network from a single platform.
Multi-year historical data
Annual, monthly, hourly and 15-minute volumes for both mid-block segments and intersections, across multiple years.
Faster access to volume data
No seasonal collection cycles, data processing or manual data entry. Volume estimates are available on-demand through the platform.
Platform-wide integration
Volume data feeds directly into all iNode modules, including risk ranking, congestion scoring and corridor analysis, from a single source of truth.
Take accuracy further with local calibration
Probe-data volume models start with national estimates that work well in aggregate. Local calibration goes further, using your agency's own ground-truth counts to retrain the model so accuracy reflects the traffic on your roads.
Ground-truth as the input
Counts from your permanent and temporary count stations become the training data that tunes the model to your region. Use the built-in self-serve calibration tool or have SMATS do it as a managed service.
Local patterns embedded
The model learns corridor-specific traffic behaviour, seasonal variation and regional characteristics that a national model averages away.
Accuracy that improves over time
Each new round of count data refines the model further. Calibration is not a one-time event, it's a continuous process.
The result: A volume-estimation system that learns from your environment
Network-wide accuracy, Powered by local truth
Manual counts capture what's happening on the ground. The calibration tool extends that local accuracy across your entire network, delivering scalable, defensible volume estimates.
Continuous improvement built into the workflow
Every new round of count collection refines the model. Accuracy becomes iterative and adaptive, always moving forward, never locked to an aging baseline.
Your agency controls its own accuracy
Model quality is driven by your own data. You define what "accurate" looks like for your region, your corridors and your planning needs, with no vendor dependency.
Significant time and resource savings
Manual model calibration is slow and often impractical. The built-in self-serve calibration tool automates the process end to end. Update and improve models in minutes, not months.
More data, More complete, More comprehensive
| Capability | Traditional Counts | Standard Big-Data Providers | SMATS iNode |
|---|---|---|---|
| Network-wide volume coverage | |||
| AADT and multi-year trend data | |||
| All road types and functional classes | |||
| No physical hardware deployment required | |||
| Local calibration, Self-serve or as a managed service | |||
| Continuous model refinement as new counts are collected | |||
| Full control over model accuracy and timing | |||
| Calibration applied across all platform modules |
Trusted by public agencies and transportation experts
Scaling traffic volumes
with Floating Car Data
Maricopa Association of Governments, Phoenix, Ariz.
MAG, the MPO for the greater Phoenix region covering five million residents, needed to validate whether Floating Car Data could reliably support regionwide volume estimation.
SMATS built a locally calibrated model and evaluated it across 219 locations. The result: R² of 0.98, mean error of -1.5% and strong alignment across all road types and time periods.
Great companies trust SMATS, your partner in smarter, data-driven mobility
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Ready to go from counting some roads to knowing every road?
See how iNode delivers network-wide volume data and how iCalibrate lets you calibrate it with your own counts for local accuracy.
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