COLUMBIA, MD / ACCESSWIRE / June 14, 2023 / Traffic data is a valuable resource often overlooked but can potentially benefit transportation systems significantly. By collecting and analyzing traffic data, transportation officials can gain insights into traffic patterns, congestion, and other factors that impact traffic flow on roadways. When this federally required information is efficiently collected, it helps cities and states access federal funding to manage and maintain America's roadways.

AI traffic data can also improve road safety by identifying accident hotspots or areas with high rates of speeding or other risky behaviors. Another valuable insight that traffic data can provide is vehicle classification; for example, if a state wants to know how many and where electric vehicles are traveling on the road, AI vehicle classification can be the primary source of accurate information.

Traffic data collection methods have not changed since the early days of the Eisenhower administration. Rubber tubes are still strewn across multilane roads, and other methods require traffic-creating lane closures to embed metal detection sensors with high failure rates. However, AI roadway intelligence is a more modern and safer way to obtain more accurate traffic data. AI technology company Rekor (NASDAQ:REKR) is changing the traffic data collection and analysis landscape. Rekor collects and connects mobility data to deliver AI roadway intelligence, laying the foundation for a digital-enabled operating system for roadways.

The Federal Government requires states and cities to report traffic data. It's how federal funding for roads is distributed back to the states. In identifying federally mandated class, count, and speed data, Rekor's AI and machine learning algorithms analyze and process video streams to accurately identify and classify vehicles based on their characteristics, such as size and speed, and can even conduct "virtual" weight in motion studies.

Rekor Discover software fully automates the safe capture of mandated Federal Highway Administration 13 vehicle classifications types, vehicle counts, and spot speed utilizing high-resolution video streams to deliver ground truth information. Unlike other analog and outdated methods, Rekor Discover operates at the side of the roadway, not in it, which makes the receipt of traffic data wholly automated and does not require deploying workers out onto busy highways and local roads.

The company's proprietary intelligent AI software and devices that are an integral part of Rekor Discover use an IoT node that can be installed in a few hours and provides valuable insight into traffic patterns that the company's Rekor One system can translate into analytics that is instrumental in making urban planning decisions and roadway enforcement. The company has two non-intrusive data collection systems that help collect these federally required data points.

The first is Rekor Edge Max, a fixed data collection system that captures up to 12 lanes of bidirectional high-speed roadway data. This system is useful for long-term traffic studies, and the two products are being tested by many state and city Departments of Transportation. These studies support the federally mandated vehicle classification process and can help states with various state-led roadway initiatives.

The second system, Rekor Edge Flex, is a non-intrusive portable data collection system that captures up to 12 lanes of high-speed roadway data. This system is most useful in understanding the environmental impact of roads for short-term traffic studies spanning 1 to 7 days.

Rekor is a company that has looked at the gaps in the current system and used the newest technology to better leverage traffic data to improve transportation systems and enhance safety for motorists and pedestrians alike.As the Infrastructure Investment and Jobs Act prioritizes transportation efficiency and safety, Rekor Discover is at the forefront of traffic data collection and analysis.

Featured photo by Aleksandr Popov on Unsplash.

Contact:

Charles Degliomini
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