StreetLight InSight is Redefining Transportation Data Collection

Unlike with household surveys, license plate studies, Bluetooth sensors - or any other Big Data analytics provider - you can access StreetLight InSight directly from your web browser to get core analyses for nearly any transportation project in minutes.* StreetLight InSight Travel Metrics are also faster to generate, more accurate, and more comprehensive than those derived from most traditional data collection techniques. Our Metrics include:

StreetLight Data's Metrics Redefine Transportation Data
  • Origin/Destination Matrices
  • Select Link Analyses
  • Average Travel Times and Travel Time Distribution
  • Internal/External Studies
  • Commercial and Personal Travel Vehicle Comparisons

Metrics can be customized to specific times of day, days of the week, and times of year. The outputs of StreetLight InSight include visualizations, shapefiles, and CSV files so that you can look at results in the app, as well as manipulate the data independently.

Ready to see StreetLight InSight in action? Watch Our Demo Videos

*Most StreetLight InSight Metrics are processed in minutes, but processing times vary project-by-project. Contact us to discuss your project in detail.

 
 

Our Metrics Development Process

Our Metrics are based on Big Data. That's the massive volume of data being created every second by mobile phones, GPS devices, connected cars, fitness trackers, and commercial fleet managements systems as their users move.

When these devices ping cell towers and satellites, they create records of the device’s location. We transform trillions of these anonymized records into useful information with our proprietary, algorithmic RouteScience® processing engine.

How Our RouteScience Processing Engine Works

Step 1: Deidentify

First, the data are reviewed to ensure all identifying information has been removed by our suppliers. We do this to to ensure individual privacy from the very beginning.

How Our RouteScience Processing Engine Works

Step 2: Clean

Next, we review the data and remove any incomplete or inaccurate data points. For example, if we have only one record for a particular device within a given time period, that data point is removed.

How Our RouteScience Processing Engine Works

Step 3: Patternize

Our next step is to algorithmically link these data points into activities and trips by identifying likely home and work locations as well as origins, destinations, and routes traveled.

How Our RouteScience Processing Engine Works

Step 4: Contextualize

Finally, we contextualize and further deidentify the data the integration of additional data sets. These additional data sets add important context that give our Metrics more meaning. They include road network maps, demographic information, parcel and land use data, and more.

How Our RouteScience Processing Engine Works

Step 5: Aggregate

Finally, we normalize and combine these trips to create aggregate Metrics. To protect individual privacy, our Metrics describe groups of devices - not individual devices.

 
 
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Validating Our Metrics

To ensure the accuracy of our Metrics, we have validated them against traditional data sources such Bluetooth-based counting technology and license plate surveys. Read more on our blog or contact us for more details.

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