Smart City Gnosys

Smart city article details

Title	Innovative Integration Of Visible Light Communication And Artificial Intelligence To Enhance Urban Traffic Management
ID_Doc	31692
Authors	Galvão G.; Vieira M.A.; Véstias M.; Louro P.; Jardim-Goncalves R.
Year	2025
Published	Proceedings of SPIE - The International Society for Optical Engineering, 13375
DOI	http://dx.doi.org/10.1117/12.3039137
Abstract	This study integrates Visible Light Communication (VLC) and Artificial Intelligence (AI) to enhance traffic signal control, reduce congestion, and improve safety through real-time data-driven management. VLC leverages existing infrastructure to transmit real-time data on vehicle and pedestrian dynamics, while AI agents employing Deep Reinforcement Learning (DRL) optimize traffic signals and vehicle trajectories across intersections. A centralized system trains a unified DRL model to coordinate local agents managing individual intersections, enabling real-time signal adjustments via a queue/request/response methodology. Simulations and real-world trials validate the approach, showing significant reductions in waiting and travel times, particularly under rerouting scenarios. Scalable to diverse intersection types, the system adapts dynamically to changing traffic conditions, improving efficiency and safety. Furthermore, the integration of VLC underscores the importance of cybersecurity in smart city infrastructures. This research aligns with CyberSecPro’s mission (https://www.cybersecpro-project.eu/), advancing cybersecurity competencies and ensuring privacy-respecting innovations in traffic management systems. The integration of VLC technology for real-time data transmission not only optimizes traffic flow but also highlights the importance of cybersecurity in smart city infrastructures ensuring secure and privacy-respecting innovations in traffic management systems. © 2025 SPIE.
Author Keywords	Connected Vehicles (CV); Cybersecurity; Deep Reinforcement Learning (DRL); Multi-Agent Systems; Queue/Request/Response Methodology; Traffic Flow Simulation; Traffic Signal Optimization; Urban Traffic Management; Visible Light Communication (VLC)

Similar Articles

Id	Similarity	Authors	Title	Published
24038	0.974	Vieira M.A.; Galvão G.; Vieira M.; Véstias M.; Louro P.; Jardim-Goncalves R.	Enhancing Traffic Flow With Visible Light Communication: A Deep Reinforcement Learning Approach	Proceedings of SPIE - The International Society for Optical Engineering, 13374 (2025)
23735	0.896	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)
18053	0.893	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)
15627	0.893	Sachan A.; Chauhan N.S.; Kumar N.	Congestion Minimization Using Fog-Deployed Drl-Agent Feedback Enabled Traffic Light Cooperative Framework	Proceedings - 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2023 (2023)
6356	0.888	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)
18032	0.883	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)
38098	0.882	Fereidooni Z.; Palesi L.A.I.; Nesi P.	Multi-Agent Optimizing Traffic Light Signals Using Deep Reinforcement Learning	IEEE Access, 13 (2025)
57625	0.881	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)
2745	0.88	Choudhary S.; Ali S.S.; Babu N.R.; Sharma H.; Kaliraman B.; Dhankhar Y.	A More Efficient Way To Control Traffic Lights Through Ai-Led Smart City Management	Proceedings - International Conference on Technological Advancements in Computational Sciences, ICTACS 2023 (2023)
25829	0.88	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)