High Performance Computing Traffic Simulations for Real-Time Traffic Control of Mobility in Chattanooga Region

Juliette Ugirumurera, Joseph Severino, Qichao Wang, Harry Sorensen, Austin Todd, Ambarish Nag, Kristin Potter, Wesley Jones, Srinath Ravulaparthy, Anne Berres, Philip Nugent, Alex Moore, Sarah Tennille, Steven Peterson, Haowen Xu, Jibonananda Sanyal

Research output: NRELPoster


In 2019, highway congestion wasted over 3 billion gallons of fuel and caused 8.8 billion hours of lost productivity.1 Research has shown that introducing near-real time traffic controls can significantly reduce congestion. Validated and calibrated traffic simulations enable the modeling of transportation systems and the evaluation of different traffic control actions and schemes given a variety of circumstances that represent likely future scenarios. The developed scenarios can inform the deployment of controls in near real-time to improve freight and passenger vehicle congestion and energy use. In this work, we present simulations used to model the traffic in the Chattanooga, Tennessee, metropolitan area. Simulations were constructed and calibrated using a variety of local, data science enhanced, data sources utilizing open source software including the Simulation of Urban Mobility (SUMO) simulator. High-Performance Computing (HPC) provides a scalable platform with enough computing for the high-fidelity simulation of many scenarios and the application of advance data science especially for large-scale systems. Our simulations include microscopic simulations at a corridor level for traffic signal control, and mesoscopic simulations to evaluate regional operational controls and infrastructure.
Original languageAmerican English
StatePublished - 2019

Publication series

NamePresented at the 2019 Tennessee Sustainable Transportation Forum & Expo, 1-2 October 2019, Knoxville, Tennessee

NREL Publication Number

  • NREL/PO-2C00-75009


  • high performance computing
  • HPC
  • real-time traffic control
  • traffic simulation


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