Smart City Gnosys

Smart city article details

Title	Traffic Light Control Using Reinforcement Learning: A Survey And An Open Source Implementation
ID_Doc	58613
Authors	Paduraru C.; Paduraru M.; Stefanescu A.
Year	2022
Published	International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS - Proceedings
DOI	http://dx.doi.org/10.5220/0011040300003191
Abstract	Traffic light control optimization is nowadays an important part of a smart city, given the advancement of sensors, IoT, and edge computing capabilities. The optimization method targeted by our work follows a general trend in the community: dynamically switching traffic light phases depending on the current traffic state. Reinforcement learning was lately adopted in the literature as it has been shown to outperform previous methods. The primary goal of our work is to provide an overview of the state of the art of reinforcement methods for traffic signal control optimization. Another topic of our work is to improve over existing tools that combine the field of reinforcement learning with traffic flow optimization. In this sense, we seek to add more output capabilities to existing tools to get closer to the domain-specific problem, to evaluate different algorithms for training strategies, to compare their performance and efficiency, and to simplify efforts in the research process by providing ways to more easily capture and work with new data sets. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.
Author Keywords	Agent; Execution Management; Open-source Tool; Optimization; Reinforcement Learning; Traffic Light Control

Similar Articles

Id	Similarity	Authors	Title	Published
57625	0.955	Ait Ouallane A.; Bahnasse A.; Bakali A.; Talea M.	Toward A Smart City: Reinforcement Learning For Traffic Light Control	Lecture Notes in Networks and Systems, 629 LNNS (2023)
25829	0.938	Thamaraiselvi K.; Bohra A.R.; Vishal V.; Sunkara P.S.; Sunku B.; Nityajignesh B.	Exploring Traffic Signal Control: A Comprehensive Survey On Reinforcement Learning Techniques	3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025 (2025)
29723	0.93	Faqir N.; Ennaji Y.; Chakir L.; Boumhidi J.	Hybrid Cnn-Lstm And Proximal Policy Optimization Model For Traffic Light Control In A Multi-Agent Environment	IEEE Access, 13 (2025)
32611	0.926	Zhu Y.; Cai M.; Schwarz C.W.; Li J.; Xiao S.	Intelligent Traffic Light Via Policy-Based Deep Reinforcement Learning	International Journal of Intelligent Transportation Systems Research, 20, 3 (2022)
44895	0.924	Barta Z.; Kovács S.; Botzheim J.	Reinforcement Learning-Based Cooperative Traffic Control System	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14811 LNAI (2024)
44891	0.919	Hu X.; He Y.	Reinforcement Learning-Based Adaptive Optimal Control Model For Signal Light In Intelligent Transportation Systems	Journal of Circuits, Systems and Computers, 34, 2 (2025)
38227	0.918	Lam H.C.; Wong R.T.K.; Jasser M.B.; Chua H.N.; Issa B.	Multi-Junction Traffic Light Control System With Reinforcement Learning In Sunway Smart City	2024 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2024 - Proceedings (2024)
40923	0.917	Zhang Z.; Zhou B.; Zhang B.; Cheng P.; Lee D.-H.; Hu S.	Optimizing Traffic Signal Control In Mixed Traffic Scenarios: A Predictive Traffic Information-Based Deep Reinforcement Learning Approach	2024 Forum for Innovative Sustainable Transportation Systems, FISTS 2024 (2024)
6356	0.915	Kumar R.; Sharma N.V.K.; Chaurasiya V.K.	Adaptive Traffic Light Control Using Deep Reinforcement Learning Technique	Multimedia Tools and Applications, 83, 5 (2024)
44887	0.914	Joo H.; Lim Y.	Reinforcement Learning For Traffic Signal Timing Optimization	International Conference on Information Networking, 2020-January (2020)