Just the right data

For decisions you can trust

Flexible data ingestion to match your use case

Floating Car Data (FCD)

Floating Car Data (FCD) is collected from GPS-enabled vehicles and mobile applications, providing real-time and historical insights into how traffic moves across an entire road network.

Bluetooth reidentification sensor data

This is data collected by sensors through Bluetooth and Wi-Fi signals emitted from smart devices.

This technology generates travel time, wait (dwell) time and an origin-destination matrix for multi-modal objects.

Radar sensor data

This is data collected by sensors that emit microwaves that reflect off moving objects and return to the receiver.

This technology generates live traffic count, classification, and queue length for multi-lane and multi-modal traffic.

High-quality data for high-confidence insight

Floating Car Data

Best-in-class penetration rate
Historical data updated daily and spanning up to 10 years
Live data transmitted within seconds
High spatial resolution, segment-level insights down to 100 feet

Bluetooth reidentification data

40-60% penetration rate
Proven filtering algorithm excluding outliers
Field-proven in extreme heat and cold

Radar sensor data

At 99% capture rate
Forward-firing radar beams enable longer object tracking compared to side-firing sensors
Higher accuracy in tracking vehicle position and speed vector measurements
More precise object classification

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.

Congestion 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 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.

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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 Floating Car 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

Smart sampling starts here

Get the minimum statistically valid sample size based on your desired confidence level, margin of error and expected variability for any traffic metrics, from travel times to volumes