Deploying a Model Predictive Traffic Signal Control Algorithm - A Field Deployment Experiment Case Study

Qichao Wang, Joseph Severino, Harry Sorensen, Jibonananda Sanyal, Juliette Ugirumurera, Chieh Wang, Andy Berres, Wesley Jones, Airton Kohls, Rajesh Paleti Ravi Venkatadurga

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

This paper presents a field deployment experiment of a real-time traffic signal control algorithm. We implemented the model predictive control (MPC) algorithm based on the virtual phase-link (VPL) model. We selected the deployment locations and times based on an energy saving potential concept. We developed a set of experiment systems, which included sensing, processing, and actuating components, to enable field deployment. We tested the systems rigorously before the experiment days. We reported the key procedures on the experiment days, including the steps taken, the real-time control procedure, and the monitoring of the experiment. We evaluated the impact of the deployment by looking at the changes in delay and energy consumption.

Original languageEnglish
Title of host publication2022 IEEE 25th International Conference on Intelligent Transportation Systems, ITSC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3564-3570
Number of pages7
ISBN (Electronic)9781665468800
DOIs
StatePublished - 2022
Event25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022 - Macau, China
Duration: Oct 8 2022Oct 12 2022

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Volume2022-October

Conference

Conference25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022
Country/TerritoryChina
CityMacau
Period10/8/2210/12/22

Funding

This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Vehicle Technologies Office. A portion of The research was performed using computational resources sponsored by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy and located at the National Renewable Energy Laboratory. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes. *This work was supported by the U.S. Department of Energy (DOE). 1Qichao Wang ([email protected]), Joseph Severino, Harry Sorensen, Juliette Ugirumurera, Jibonananda Sanyal, and Wesley Jones are with Computational Science Center at the National Renewable Energy Laboratory in Golden, Colorado, USA. 2Chieh (Ross) Wang, Andy Berres, and Rajesh Paleti Ravi VenkataDurga are with Oak Ridge National Laboratory in Oak Ridge, Tennessee, USA. 3Airton Kohls is with the University of Tennessee – Knoxville in Knoxville, Tennessee, USA.

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