October 6, 2021
Integrated Data Is Reliable Data


This post is Part 2 of our series on the critical advantages of data integration. Read Part 1 here.


In traffic management, every data source can provide you with critical, unique information, but they also all have limitations. Connected vehicle (CV) data provides precise information about individual vehicles over their entire journey, but there aren’t enough CVs on the roads today to provide a complete picture. Sensor and signal detection data provide substantial information about what’s happening at a particular point in the roadway, but this data is often unreliable, and hardware can’t be placed everywhere. So, at Flow Labs, we’re combining both.

At Flow Labs, our Virtual Sensor technology integrates data sources together to maximize data reliability.

As a hardware free platform, we can “place” a virtual sensor at any point on a roadway, or across the entire roadway itself. Then, we direct the virtual sensor to begin collecting data from nearby sources. We collect data from nearby detectors and IoT sensors. We collect data from nearby traffic signals. We collect data from nearby connected vehicles. Then, our proprietary artificial intelligence seamlessly fuses these datasets together, eliminates data inaccuracies, and correctly shows how many vehicles there are, and what they’re doing.  

Our virtual sensor technology is also future-proof. We’ve made a platform that can incorporate any data source while negating its disadvantages.

Our virtual sensors are highly accurate.

We put our virtual sensors to the test by working with the Utah Department of Transportation (UDOT) to conduct a study of our virtual sensors when compared to their detectors in the field. 

The results proved that we’re generating measurements closer to the actual counts than the measurements gathered from any singular source. Our virtual sensors were 94.4% accurate. They outperformed detectors everywhere they were placed, generating more accurate results in areas with high detector volumes, low detector volumes, and, of course, in areas with no detectors at all. Additionally, our sensors had a GEH score, another commonly used metric to determine the rate of error, of 1.6. Any number under 5 is considered acceptable. The physical detectors scored a 3.5. 

Most impressively, we achieved these results in areas with only 0 and 3.2% of the cars registering as connected vehicles. As millions of CVs join our roadways each year, we’ll be able to gather even more GIS data, and our measurements will become more reliable. We’ll also continue to add new datasets, such as additional forms of sensors. 

You can read our full case study here.

Our virtual sensor technology is scalable, low cost, and can be used right now.

Every new piece of detection hardware costs an agency over $10,000 and requires months to implement. In contrast, you can start using our virtual sensors immediately. You can place as many sensors as you need, wherever you want them. You can collect better data, and more of it, for 1/300th of the cost. 

With reliable data, we’re providing effortless, proactive, affordable signal management today. Next in this series, we’ll discuss how we’ve leveraged these sensors to produce the most accurate, comprehensive, and relevant analytics in the industry.
To see our virtual sensor technology in action, please contact us to schedule a demo at sales@flowlabs.ai.
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