Adaptive Urban Traffic Signal Control for Multiple Intersections: An LQR Approach

Jiho Park, Tong Liu, Chieh Wang, Andy Berres, Joseph Severino, Juliette Ugirumurera, Airton Kohls, Hong Wang, Jibonananda Sanyal, Zhong-Ping Jiang

Research output: Contribution to conferencePaperpeer-review

2 Scopus Citations

Abstract

Traffic congestion leads to severe problems especially in urban traffic networks. It increases the chance of accidents, energy waste, and social costs. In order to address these problems, an adaptive linear quadratic regulator (LQR) approach is developed for traffic signal control at multiple intersections in an urban area. The proposed method controls the green time of the traffic signals to reduce traffic congestion and smooth traffic flow. Real-world data from vision-based traffic sensors are used to build the traffic network model, which mimics the real-world traffic behavior. In addition, the proposed control utilizes recursive least square parameter estimation, which is capable of tracking dynamic changes in traffic conditions. Simulation of Urban MObility (SUMO) is used to analyze the efficacy of the proposed method. Results of the simulation show that the proposed method outperforms pretimed control in various aspects.

Original languageAmerican English
Pages2240-2245
Number of pages6
DOIs
StatePublished - 2022
Event25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022 - Macau, China
Duration: 8 Oct 202212 Oct 2022

Conference

Conference25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022
Country/TerritoryChina
CityMacau
Period8/10/2212/10/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

NREL Publication Number

  • NREL/CP-2C00-85000

Keywords

  • adaptive LQR control
  • GRIDSMART camera
  • model predictive control (MPC)
  • recursive least square
  • SUMO simulation
  • Traffic signal control

Fingerprint

Dive into the research topics of 'Adaptive Urban Traffic Signal Control for Multiple Intersections: An LQR Approach'. Together they form a unique fingerprint.

Cite this