Smart City Gnosys

Smart city article details

Title	Enhancing Adaptive Traffic Control Systems With Deep Reinforcement Learning And Graphical Models
ID_Doc	23735
Authors	Sattarzadeh A.R.; Pathirana P.N.
Year	2024
Published	Proceedings - 2024 IEEE International Conference on Future Machine Learning and Data Science, FMLDS 2024
DOI	http://dx.doi.org/10.1109/FMLDS63805.2024.00017
Abstract	Intelligent traffic signal control plays a crucial role in improving urban transportation networks by reducing congestion and increasing traffic flow efficiency. This paper presents a deep reinforcement learning (DRL) approach for managing traffic signals at multiple intersections. This method enhances both the adaptability and efficiency of traffic control systems. Our model integrates a probabilistic graphical framework with DRL, facilitating the extraction of interpretable and actionable insights from complex traffic data. We have developed and validated our model using a simulated environment that reflects real-world traffic conditions, demonstrating substantial improvements in traffic throughput and reduction in average travel times. The proposed model outperforms traditional traffic signal control algorithms. It also offers a robust framework for real-time traffic management, contributing to the development of future smart city infrastructure. The model achieves a 95% success rate, reduces average queue lengths by over 50%, and cuts intersection delays by up to 80%, compared to traditional methods. These improvements highlight its efficiency in optimizing traffic flow and minimizing congestion. © 2024 IEEE.
Author Keywords	Adaptive Traffic Systems; Deep Reinforcement Learning (DRL); Intelligent Transportation Systems (ITS); Traffic Signal Control

Similar Articles

Id	Similarity	Authors	Title	Published
43250	0.948	Sattarzadeh A.R.; Pathirana P.N.	Probabilistic Graph Models: A Key To Boosting Deep Reinforcement Learning In Urban Traffic Networks	2025 17th International Conference on Computer and Automation Engineering, ICCAE 2025 (2025)
18032	0.944	Singh D.	Deep Reinforcement Learning (Drl) For Real-Time Traffic Management In Smart Cities	2023 International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023 (2023)
18057	0.934	Mittal M.; Sehgal A.; Varshney N.; Kumar S.P.; Boob N.S.; Reddy R.A.	Deep Reinforcement Learning For Optimizing Route Planning In Urban Traffic	IEEE International Conference on "Computational, Communication and Information Technology", ICCCIT 2025 (2025)
25829	0.93	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)
40923	0.928	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)
38098	0.927	Fereidooni Z.; Palesi L.A.I.; Nesi P.	Multi-Agent Optimizing Traffic Light Signals Using Deep Reinforcement Learning	IEEE Access, 13 (2025)
6362	0.924	Gupta A.; Agarwal A.; Bohara V.A.	Adaptive Traffic Signal Cycle Control For Green City Traffic Management	IEEE Region 10 Annual International Conference, Proceedings/TENCON (2024)
6368	0.923	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)
18053	0.92	Kansal V.; Shnain A.H.; Deepak A.; Rana A.; Manjunatha; Dixit K.K.; Rajkumar K.V.	Deep Reinforcement Learning For Iot-Based Smart Traffic Management Systems	Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2024 (2024)
21429	0.917	Skoropad V.N.; Deđanski S.; Pantović V.; Injac Z.; Vujičić S.; Jovanović-Milenković M.; Jevtić B.; Lukić-Vujadinović V.; Vidojević D.; Bodolo I.	Dynamic Traffic Flow Optimization Using Reinforcement Learning And Predictive Analytics: A Sustainable Approach To Improving Urban Mobility In The City Of Belgrade	Sustainability (Switzerland), 17, 8 (2025)