Just the right data
For decisions you can trust
SMATS draws on multiple data sources depending on the use case, deployment context, and level of precision required.
Each source has distinct strengths. Most deployments combine more than one.
Passenger car probe data
GPS probe data collected passively from connected vehicles, navigation systems, fleet telematics, and mobile devices. Passenger car probe data covers every road on the network continuously, with no hardware installation required. It is the primary source for network-wide travel time, speed, corridor performance, origin-destination analysis, and volume estimation.
Best for:
Network-wide coverage, historical analysis, OD studies, volume estimation, signal performance, congestion monitoring.
Key specs:
Updated daily, up to 10 years of historical depth, live data within seconds.
Bluetooth and wi-Fi sensors (TrafficXHub)
Roadside sensors that passively detect anonymized Bluetooth and Wi-Fi signals from devices in passing vehicles. TrafficXHub sensors measure travel time and origin-destination patterns at specific locations with high precision, and are the primary source for wait time monitoring, queue detection, and audience measurement at fixed points.
Best for:
Work zone monitoring, waste facility wait times, port gate wait times, OOH audience measurement, travel time on specific corridors where ground-truth precision is required.
Key specs:
40-60% vehicle penetration rate
Radar sensors
Smartmicro radar sensors provide real-time vehicle counts and classification at specific locations, pedestrians, cyclists, motorcycles, passenger cars, trucks, buses, and long trucks across seven classes. Radar data feeds directly into iNode and supports intersection-level volume counts, turning movement counts, and classification studies.
Best for:
Vehicle classification, intersection counts, turning movement counts, locations requiring precise volume data by vehicle type.
Key specs:
Up to 99% capture rate, seven vehicle classes.
Truck probe data
First-party GPS fleet data collected directly from commercial vehicles via GPS units and electronic logging devices. Maintained as a separate dataset from passenger car probe data, truck probe data provides origin-destination flows, stop analytics, and corridor-level performance metrics specifically for commercial vehicles across North American road networks.
Best for:
Freight planning, truck OD studies, truck AADT, VMT by vehicle class, stop and parking analytics, cut-through truck route detection.
Key specs:
Classified by vehicle class and vocation.
How they work together
| Use case | Passenger car probe data | Bluetooth/Wi-Fi | Radar | Truck probe data |
|---|---|---|---|---|
| Network-wide congestion monitoring | ||||
| Work zone travel time and VMS | ||||
| Port gate wait time and yard movement | ||||
| Waste facility queue monitoring | ||||
| Signal performance and corridor studies | ||||
| Volume estimation and counts | ||||
| OD and traffic planning | ||||
| Road safety studies | ||||
| OOH audience measurement | ||||
| Freight planning and truck OD | ||||
| Insurance territory pricing | ||||
| Private site traffic management |
Metrics you can trust, no matter the use case
Supplemental Data Collection to Fill in the Detection and Metric Gaps
Bluetooth Reidentification Sensor Data
This is data collected by sensors through Bluetooth and Wi-Fi signals emitted from smart devices.
This technology generates live travel time, wait (dwell) time and origin destination matrix for multi-modal objects. At 40-60% capture rate, it fills the gap for data collection in areas without cloud-based big data (e.g. ports).
Radar Sensor Data
This is data collected by sensors that emit microwave that reflect off moving objects and return to the receiver.
This technology generates live traffic count, classification, queue length for multi-lane and multi-modal traffic at 99% capture rate.
Traffic operation metrics
Support both real-time and planning analysis.
Includes:
- Travel time-based metrics (TTI, PTI, BI)
- Speed-based metrics
- Delays
- Level of Service (LOS) for intersections and corridors
- Queue lengths
Origin-destination
Analyze movement between links and zones using:
- Trip counts and origin-destination matrices
- Trip durations and length distributions
- Travel times and speeds between origin and destination links or zones
Safety metrics
Estimated using SMATS’ iNode platform with AI-based volume modeling.
Includes:
- Mid-block and intersection volumes
- Turning Movement Counts (TMC)
- Monthly Average Daily Traffic (MADT)
- Annual Average Daily Traffic (AADT)
- Mean, median and harmonic average and percentile speeds
Signal performance metrics
Aggregated by approach and movement for signalized intersections.
Includes:
- Level of Service (LOS)
- Control delay and stopped delay
- Arrival on Green (AOG) percentage
- Split failure rate
Speeds
Analyze segment-level speeds using:
- Average speeds
- Percentile speeds (e.g., 5th to 95th percentile)
- Mean, median and harmonic average speeds
Travel times
Measure average travel times between custom points, individual road segments, or links between signalized intersections.
Includes Travel Time Reliability (TTR) metrics such as:
- Travel Time Index (TTI)
- Planning Time Index (PTI)
- Buffer Index (BI)
Volumes and classifications
Estimated based on observed sample counts using passenger car probe data
Volumes Includes
- Mid-block and intersection volumes
- Turning Movement Counts (TMC)
- Monthly Average Daily Traffic (MADT)
- Annual Average Daily Traffic (AADT)
Powered by smartmicro’s ultra-high-definition 4D radar and AI-based classification algorithms, objects are identified based on size, length, and movement characteristics. Seven classifications are supported, including:
- Pedestrians
- Bicycles
- Motorcycles
- Passenger cars
- Vans/transporters
- Trucks or buses
- Long truck
Built by traffic engineers. Shared through insight.
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