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Data-Driven Simulations Mirror a City’s Traffic Patterns to Reduce Mobility-Related Energy Use

NREL is Participating in a Project Led by Oak Ridge National Lab to Create a Large-Scale, Real-Time “Digital Twin” of Traffic Conditions in Chattanooga, Tennessee

Feb. 14, 2019

Traffic is frustrating—it wastes precious time and fuel, and its patterns are complex and difficult for drivers and city planners to predict.

A new project funded by the U.S. Department of Energy’s Vehicle Technologies Office and led by Oak Ridge National Laboratory (ORNL), in partnership with the National Renewable Energy Laboratory (NREL), the city of Chattanooga, Tennessee, and the Tennessee Department of Transportation, hopes to find solutions through leveraging the power of high-performance computing.

In this video, ScienceNode.org talks to NREL Computational Scientist Juliette Ugirumurera about her work on an ORNL-led project to create a large-scale, real-time simulation of traffic in Chattanooga, Tennessee.

The project involves creating a “digital twin” of the entire Chattanooga metropolitan region—a data-informed simulation that captures real-time traffic conditions of the region via sensors installed on roads. This dynamic traffic simulation aims to help investigate the causes of traffic congestion and come up with effective management strategies city officials can apply.

As profiled in a recent story on ScienceNode.org, NREL Computational Scientist Juliette Ugirumurera and her team are working to optimize these complex, large-scale simulations by running them on high-performance computers including the Cori supercomputer at the National Energy Research Scientific Computing Center and the Eagle supercomputer at NREL. These machines help hasten the transformation of complex data into insights that can be used to inform decision makers on energy-saving solutions.

Read the full story about the project on ScienceNode.org, and learn more about NREL’s computational science and transportation research.