TY - GEN
T1 - Adaptive Urban Traffic Signal Control for Multiple Intersections
T2 - 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022
AU - Park, Jiho
AU - Liu, Tong
AU - Wang, Chieh
AU - Berres, Andy
AU - Severino, Joseph
AU - Ugirumurera, Juliette
AU - Kohls, Airton G.
AU - Wang, Hong
AU - Sanyal, Jibonananda
AU - Jiang, Zhong Ping
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - GRIDSMART camera
KW - SUMO simulation
KW - Traffic signal control
KW - adaptive LQR control
KW - model predictive control (MPC)
KW - recursive least square
UR - http://www.scopus.com/inward/record.url?scp=85141831090&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85141831090&partnerID=8YFLogxK
U2 - 10.1109/ITSC55140.2022.9922033
DO - 10.1109/ITSC55140.2022.9922033
M3 - Conference contribution
AN - SCOPUS:85141831090
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 2240
EP - 2245
BT - 2022 IEEE 25th International Conference on Intelligent Transportation Systems, ITSC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 8 October 2022 through 12 October 2022
ER -