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Each state is mandated by federal transportation authorities to use traffic management systems (TMS) to track the number of cars traveling on the road, the type of vehicle and its weight. That data collectively helps promote efficient travel and keep people safer.

However, the systems in place now are expensive, and depending on the type installed, not always possible to move.

The current options are intrusive sensors embedded into the pavement, nonintrusive sensors that sit on top of or over the roadways and off-roadway sensors, such as satellites. However, a research team at the University of Memphis proposes a different system using Wi-Fi signals and deep learning. They say the system they’ve developed is low-cost and portable, potentially making it an excellent TMS to use in the future.

How Does It Work?

The technology is a nonintrusive system called DeepWiTraffic that tracks and classifies vehicles based on the channel state information (CSI) of the automobiles that move past. CSI encompasses the properties of a wireless communication link.

The team figured out a method of determining how the vehicles that go past cause changes in specific CSI information, including spatial and time correlations. That data is used to classify the kinds of vehicles and how much …

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