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

Conference · Tue Nov 01 04:00:00 EDT 2022

DOI:https://doi.org/10.1109/ITSC55140.2022.9922033· OSTI ID:1909416

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

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.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 1909416
• Report Number(s): NREL/CP-2C00-85000; MainId:85773; UUID:8562ec10-623b-4a3c-8e6d-598cf81f4efe; MainAdminID:68457
• Country of Publication: United States
• Language: English

Related Subjects

GRIDSMART camera
MATHEMATICS AND COMPUTING
SUMO simulation
adaptive LQR control
model predictive control (MPC)
recursive least square
traffic signal control