Truck probe data
First-party GPS fleet data for freight planning, truck origin-destination analysis, stop analytics, and corridor-level commercial vehicle metrics.
Freight planning decisions are only as good as the truck data behind them.
Standard traffic data treats all vehicles the same. Freight planning requires something different: data that separates commercial vehicles from passenger cars, shows where trucks actually originate and where they go, reveals where they stop and for how long, and measures their performance on specific corridors by vehicle class. SMATS provides truck-specific data from first-party GPS fleet sources, covering 99.9% of primary roadways in North America. Three situations where this data changes what is possible.
Freight plans built on modelled estimates are difficult to defend
State freight plans, grant submissions, and FHWA HPMS reporting all require defensible data. Modelled estimates and mixed-traffic counts cannot distinguish commercial vehicle behavior from the broader traffic stream. First-party GPS fleet data, classified by vehicle weight, gives freight planners the truck-specific evidence base needed to justify corridor investments and funding applications.
Where trucks go after leaving a major facility is rarely visible
Gate counts tell you how many trucks entered or exited; not where they came from, which routes they used, or which residential streets they passed through. Origin-destination analysis with trip chaining across full duty cycles maps the complete journey, not just the gate event.
Truck parking shortages are documented through complaints, not data
Most planning efforts still rely on driver surveys and anecdotal reports. Stop analytics from GPS fleet data shows where commercial vehicles are actually stopping, how long they remain parked, and how demand varies by time of day, replacing guesswork with observation.
Our solutions
Truck origin-destination (OD) analysis
Understand where commercial vehicles come from, which routes they take, and where they go, with trip chaining across full duty cycles.
- Truck OD trips maintained separately from passenger vehicle data throughout
- Trip chaining across 24+ hours using persistent vehicle identifiers, reconstructing full journeys not just individual legs
- OD trip tables by user-defined zones, including shapefile upload, with select link and pass-through analysis
- Cut-through route detection: identify commercial vehicles using residential streets to bypass congested arterials
- Daily, seasonal, and annual temporal resolution
Truck traffic analytics and stop analytics
Corridor-level commercial vehicle performance data and parking behavior, for freight planning, safety analysis, and infrastructure investment.
- Truck-specific speed, travel time, and AADT by corridor and vehicle class, at 15-minute resolution
- Vehicle miles traveled (VMT) by vehicle class and vocation for corridor wear, freight intensity, and emissions planning
- Idling metrics by vehicle class and location to target congestion mitigation and air quality improvements
- Stop locations and dwell times across rest areas, commercial sites, and informal stops, with hourly parked event data
- Before/after studies and trend analysis for freight corridors using historical data going back several years
Key metrics
All metrics are derived from first-party GPS fleet data, maintained as a truck-specific sample separate from passenger vehicle data throughout.
Truck AADT
Annual average daily traffic for commercial vehicles at segment level. Available for HPMS reporting, capacity analysis, and infrastructure investment justification.
VMT by vehicle class
Vehicle miles traveled broken down by vehicle weight category and vocation. Supports corridor wear analysis, freight intensity mapping, and sustainability planning.
Truck OD flows
Aggregated commercial vehicle origin-destination trip tables by user-defined zone. Daily, seasonal, and annual temporal resolution with select link and pass-through analysis.
Truck speed and travel time
Corridor-level commercial vehicle speed and travel time by vehicle class. Available at 15-minute resolution, aggregated hourly, daily, and monthly.
Idling metrics
Idling hotspots by vehicle class and location, showing where commercial vehicles spend time stationary in traffic. Supports congestion mitigation and air quality planning.
Stop duration and frequency
Where trucks stop, how long they remain parked, and how demand varies by time of day. Separated from brief pauses to reflect true parking behavior.
Common use cases
How transportation agencies, consultants, and freight planners use truck data from SMATS.
State and regional freight plans
First-party truck OD data and corridor metrics provide the defensible, vehicle-classified evidence base needed for federally compliant freight plans and grant submissions under NHFP, RAISE, and INFRA programs.
Cut-through truck traffic analysis
OD analysis identifies commercial vehicles using residential streets to avoid congested arterials, providing documented route frequency data to support traffic calming measures or access restrictions.
Transportation model calibration
Truck OD data provides real-world commercial vehicle trip patterns as direct input into simulation platforms, calibrating models against observed freight behavior rather than synthetic or assumed OD matrices.
Freight parking planning
Stop analytics show where truck parking demand actually concentrates, when it peaks, and how long vehicles remain parked, replacing driver surveys with observed GPS data for rest area and facility planning.
Port and logistics access analysis
Truck OD flows show where commercial vehicles originate before reaching a port or distribution facility, which access corridors carry the heaviest freight load, and which surrounding streets absorb overflow traffic.
Freight corridor safety studies
Truck-specific speed data flags corridors where commercial vehicles exceed posted limits. Before/after analysis measures the impact of speed enforcement or infrastructure countermeasures on freight corridor safety.
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