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 | English |
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Title of host publication | 2022 IEEE 25th International Conference on Intelligent Transportation Systems, ITSC 2022 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 2240-2245 |
Number of pages | 6 |
ISBN (Electronic) | 9781665468800 |
DOIs | |
State | Published - 2022 |
Event | 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022 - Macau, China Duration: Oct 8 2022 → Oct 12 2022 |
Publication series
Name | IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC |
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Volume | 2022-October |
Conference
Conference | 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022 |
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Country/Territory | China |
City | Macau |
Period | 10/8/22 → 10/12/22 |
Funding
This work is supported partly by the U.S. Department of Energy (DOE), Vehicle Technologies Office, Energy Efficient Mobility Systems (EEMS) Program and partly by the NSF under Grant EPCN-1903781.
Keywords
- GRIDSMART camera
- SUMO simulation
- Traffic signal control
- adaptive LQR control
- model predictive control (MPC)
- recursive least square