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Researchers Rev Up Innovative ML Strategies to Reclaim Energy, Time, and Money Lost in Traffic Jams

A traffic jam.

Recent research by the U.S. Department of Energys National Renewable Energy Laboratory, in partnership with Oak Ridge National Laboratory, reveals the potential to untangle traffic snarls through a combination of next-generation sensors and controls with

Credit: Raj K. Raj/HT

A research team led by the National Renewable Energy Laboratory (NREL) found that next-generation sensors and controls in combination with high-performance computing, analytics, and machine learning could minimize road congestion.

The researchers used real-time data gathered by a wide range of sensors to develop a series of simulations of traffic conditions in Chattanooga, TN, and identify which controls can achieve the greatest energy efficiency while optimizing travel time, highway speed, and safety.

The researchers also analyzed the underlying causes of congestion using machine learning, data from GPS devices and vehicle sensors, and visual analytics.

The data could help urban planners, technology developers, automakers, and fleet operators design systems and equipment to make commutes and deliveries more efficient.

NREL's Juliette Ugirumurera said, "The intersection of high-performance computing, high-fidelity data, machine learning, and transportation research can deliver powerful results, far beyond what has been possible in the past with legacy technology."

From National Renewable Energy Laboratory
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