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 language | American English |
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Pages | 2240-2245 |
Number of pages | 6 |
DOIs | |
State | Published - 2022 |
Event | 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022 - Macau, China Duration: 8 Oct 2022 → 12 Oct 2022 |
Conference
Conference | 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022 |
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Country/Territory | China |
City | Macau |
Period | 8/10/22 → 12/10/22 |
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