Smart City Gnosys

Smart city article details

Title	Reinforcement Learning-Based Adaptive Optimal Control Model For Signal Light In Intelligent Transportation Systems
ID_Doc	44891
Authors	Hu X.; He Y.
Year	2025
Published	Journal of Circuits, Systems and Computers, 34, 2
DOI	http://dx.doi.org/10.1142/S021812662550046X
Abstract	Day-to-day mobility among the population has increased with economic growth. Smart cities are renovated with advanced technologies to admire modern life in which intelligent transportation becomes highly focused. Because the traffic signal control systems are fixed at a constant time. They split the traffic signal into predetermined intervals and function inefficiently; they result in long wait times, waste fuel and increased carbon emissions. This research study introduces a novel technique for traffic light management to reduce the uncertainties in the system. A dynamic and intelligent traffic light adaptive optimal management system (DITLAOCS) is implemented in this research. It does this by modifying the traffic signal duration in run time and using real-time traffic data as input. Furthermore, the proposed DITLAOCS executes based on three modes: fairness mode (FM), priority mode (PM) and emergent mode (EM). In fairness mode (FM), all vehicles are prioritized equally, while vehicles in different categories receive varying priority levels. Emergency vehicles, on the other hand, receive the highest priority. Furthermore, a fuzzy inference method based on traffic data is shown to choose one mode out of three (FM, PM and EM). This model uses deep reinforcement learning to switch traffic lights in three different phases (red, green and yellow). We evaluated and accurately simulated DITLAOCS on the Shaanxi city map in China using Simulation of Urban MObility (SUMO), an open-source simulator. The simulation results illustrate the efficiency of DITLAOCS when compared to other cutting-edge algorithms on several performance measures. © 2025 World Scientific Publishing Company.
Author Keywords	deep learning; dynamic traffic light control; ideal management fuzzy logic; intelligent traffic signal system (ITS); Reinforcement learning

Similar Articles

Id	Similarity	Authors	Title	Published
40767	0.925	Seifivand S.M.; Asghari P.; Javadi H.H.S.; Nourmohammadi H.	Optimizing And Managing The Lighting Time Of The Traffic Light Using The Reinforcement Learning System Based On Fuzzy Logic And Training The System With Evolutionary Algorithms	International Journal of Intelligent Transportation Systems Research (2025)
58613	0.919	Paduraru C.; Paduraru M.; Stefanescu A.	Traffic Light Control Using Reinforcement Learning: A Survey And An Open Source Implementation	International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS - Proceedings (2022)
57625	0.919	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)
32611	0.912	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)
6356	0.911	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)
29723	0.902	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)
38227	0.901	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)
23735	0.898	Sattarzadeh A.R.; Pathirana P.N.	Enhancing Adaptive Traffic Control Systems With Deep Reinforcement Learning And Graphical Models	Proceedings - 2024 IEEE International Conference on Future Machine Learning and Data Science, FMLDS 2024 (2024)
44882	0.896	Bohra A.R.; Selvi T.; Vishal V.; Sunkara P.S.; Sunku B.; Jignesh B.N.	Reinforcement Learning For Adaptive Traffic Signal Control Using Deep Q-Networks	1st International Conference on Sustainable Energy Technologies and Computational Intelligence: Towards Sustainable Energy Transition, SETCOM 2025 (2025)
6368	0.895	Dong Y.; Huang H.; Zhang G.; Jin J.	Adaptive Transit Signal Priority Control For Traffic Safety And Efficiency Optimization: A Multi-Objective Deep Reinforcement Learning Framework	Mathematics, 12, 24 (2024)