StreetLight Data unveils accurate on-demand Traffic Counts for 4 million miles of roadway
Photo Credit To Jeff Turner

StreetLight Data unveils accurate on-demand Traffic Counts for 4 million miles of roadway

StreetLight Data unveils accurate on-demand Traffic Counts for 4 million miles of roadway

StreetLight Data the leader in big data analytics for mobility, today announced another major transportation market innovation with the launch of its new Annual Average Daily Traffic (AADT) metrics available on-demand through the StreetLight InSight platform.

Annual traffic counts available on-demand for any roadway in the U.S. are a holy grail for transportation planners and engineers. Historically they have had to task staff or consultants with manually counting, installing equipment or modelling traffic, which isn’t feasible or accurate for understanding road congestion at scale.

For most roadways, we collect AADT using temporary traffic count sensors that operate for two to seven days with limited information. Using advanced modelling techniques, we turn a few days of real-world data into an estimate of average daily traffic for an entire year,” said Stephen S. Yoon, Senior Regional Planner at SCAG, the largest metropolitan planning organization in the U.S. “Unfortunately, most public agencies lack the resources to install and maintain permanent counters on every roadway they want to measure.”

“We measure and analyse the movements of millions of vehicles across 4 million miles of U.S. roadway, collecting and processing 365 days of traffic data,” explained Laura Schewel, CEO and co-founder of StreetLight Data. “Years of fine-tuning our proprietary algorithms, powered by machine learning and deep mobility analytic techniques, give our 2017 AADT metrics unmatched accuracy.”

StreetLight Data AADT Correlation Image
StreetLight Data AADT Correlation Image

Available for any road – big and small, rural to urban areas – the company’s 2017 AADT counts outperform industry-standard accuracy targets, as published recently in StreetLight’s 2017 AADT Methodology and Validation white paper. Data from over 2,400 permanent count stations across the U.S. were used to train and validate the big data algorithms behind 2017 AADT.

The diversity and accuracy of data feeding StreetLight’s AADT algorithms are a key competitive advantage. They include 365 days of data from over 65 million smart phones, connected cars and connected trucks, as well as census data, road network information, and more. StreetLight’s data resources are larger and more spatially accurate than cellular data used by competitors.

With new federal requirements put in place by the “Moving Ahead for Progress in the 21st Century Act” (MAP-21), local and state government agencies must increase reporting of performance metrics for planning and funding allocation. This, in turn, has increased the need for quick, easy, dynamic and cost-effective AADT measurements.

“An accurate count of vehicles on the road is the fundamental building block for so much of what we need to measure about transportation – safety, environmental impact, congestion impact, and more. Our 2017 AADT metric is the first step among many exciting new analytics our data science team is building,” said CEO Laura Schewel.

StreetLight Data pioneered the use of big data analytics to help transportation professionals solve their biggest problems. Applying proprietary machine learning algorithms to over 4 trillion mobile location data points, the company measures diverse travel patterns and makes them available on demand via the world’s first SaaS platform for mobility, StreetLight InSight. From identifying sources of congestion to optimizing new infrastructure to planning for autonomous vehicles, StreetLight powers more than 1,500 projects globally every month.

Post source : StreetLight Data, Inc.

About The Author

Anthony has worked in the construction industry for many years and looks forward to bringing you news and stories on the highways industry from all over the world.

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