Use Big Data for Transportation Planning with StreetLight InSight®

We deliver on-demand access to real-world transportation analytics via StreetLight InSight, our easy-to-use online platform. StreetLight InSight puts the best Big Data resources and data processing software at your fingertips.

Set Up and Run Transportation Studies On Your Computer

StreetLight InSight lets you design and run your own transportation analyses with your web browser ‐ on demand. There's no software installation, sensor deployment, or survey design required. You can choose the specific dates, hours of the day, and even the types of trips you want to study.  Plus, StreetLight InSight Metrics are more comprehensive, precise, and up-to-date than most traditional data resources.
Our Metrics include: 

Origin-Destination Matrices

Trip Purpose

Select Link Analyses

Average Travel Times and Travel Time Distributions

2016 AADT

Commercial and Personal Travel Vehicle Comparisons

Metrics can be customized to specific times of day, types of day, and even to exact calendar dates. StreetLight InSight
provides visualizations, shapefiles, and CSV files.

Our Metrics Development Process

StreetLight InSight Metrics are based on Big Data. That's the massive volume of geospatial information created by mobile phones, GPS devices, connected cars and commercial trucks, fitness trackers, and more. When these devices ping cell towers and satellites, they create location records.

We transform trillions of these anonymized records into useful information with our proprietary algorithmic processing engine, Route Science®When you use StreetLight InSight, you tap into the useful information that Route Science pulls from Big Data. And you avoid the hassle of manually processing trillions of location records into travel patterns.  

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.

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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.

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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.

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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.

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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.

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Video Testimonial: Fehr & Peers

Why Fehr & Peers' CEO Matt Henry believes
Big Data is valuable for the future of transportation.

WATCH NOW

Blog: Traffic Congestion Studies

Why our Big Data analytics are better for studying and managing traffic congestion than many traditional data collection tools.

READ NOW

Article: Travel Demand Modeling

How our Big Data analytics can help you build better travel demand models and more accurate forecasts of transportation behavior.

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Have Questions? Get Answers During Our Weekly Webinar.

Our 30-minute introduction to StreetLight InSight covers all the basics. Join us on Wednesdays at 10:00 AM Pacific.