StreetLight InSight® for Transportation

Transportation engineers and urban planners need accurate, precise, and comprehensive information about how people move through their communities. With our easy-to-use StreetLight InSight platform, transportation experts can understand travel patterns in just a few mouse clicks.

StreetLight InSight is Redefining Transportation Data Collection

What if you could set up and run transportation studies right on your computer? Unlike with household surveys, license plate studies, sensors, or any other Big Data analytics provider, StreetLight InSight lets you collect travel behavior data with your web browser. There's no software installation, sensor deployment, or survey design required. 

StreetLight InSight Metrics are not only faster and easier to collect. They're also more accurate, and more comprehensive than analytics derived from most traditional data resources. Our Metrics include:

  • Origin/Destination Matrices,
  • Select Link Analyses,
  • Average Travel Times and Travel Time Distribution,
  • Internal/External Studies, and
  • 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 created every second by mobile phones, GPS devices, connected cars, fitness trackers, and commercial fleet management systems.

These devices ping cell towers and satellites, creating records of their locations. We transform trillions of these anonymized records into useful information with our proprietary, algorithmic processing engine, RouteScience®.

How Our RouteScience Processing Engine Works

Step 1: Deidentify

First, the data is reviewed to ensure all personally identifying information (PII) has been removed by our suppliers. We do this to ensure individual privacy from the beginning, and we do not possess any PII. 

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. We then can identify likely home and work locations as well as origins, destinations, and routes traveled.

How Our RouteScience Processing Engine Works

Step 4: Contextualize

We then contextualize and further de-identify the data, as well as integrate additional data sets. These additional data sets 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 into aggregate Metrics. To protect individual privacy, our Metrics only describe groups of devices. They never describe individuals.

Validating Our Metrics

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

Read Our Blog Post
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FREE GUIDE to Unlocking Big Data's Value with Real-World Examples

With Big Data, transportation experts can answer questions about travel behavior that were once unanswerable. Download our "Big Data for Transportation" eBook to learn more.

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