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Supercomputing for Better Commuting: In Pursuit of Fuel Economy, Mobility


A GRIDSMART traffic camera installed at an intersection in Leesburg, VA.

Researchers at Oak Ridge National Laboratory are working with GRIDSMART Technologies to program traffic signals to improve automotive fuel economy and reduce emissions, while facilitating the flow of traffic.

Credit: GRIDSMART

Oak Ridge National Laboratory (ORNL) researchers are working with GRIDSMART Technologies to demonstrate how traffic lights can be programmed to improve automotive fuel economy and reduce emissions, while facilitating the flow of traffic.

The researchers are using computer vision, machine learning, and sensors to teach GRIDSMART cameras to estimate the fuel economy of vehicles at intersections and then to control traffic signal timing in order to save energy while optimizing traffic throughput.

The project is part of the U.S. Department of Energy's High-Performance Computing for Mobility program, which brings together supercomputing resources and scientific expertise to find solutions to real-world transportation energy challenges.

ORNL's Tom Karnowski said, "The whole idea is to teach cameras to estimate fuel consumption and then teach an entire grid of those cameras to manage traffic lights to make the system more fuel-efficient."

From Oak Ridge National Laboratory
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