Smart City Gnosys

Smart city article details

Title	Decentralized Traffic Management Through A Hybrid Incremental And Federated Learning Approach
ID_Doc	17678
Authors	Ficili I.; Tricomi G.; Cicceri G.; Longo F.; Vitabile S.; Puliafito A.
Year	2025
Published	Proceedings - 2025 IEEE International Conference on Smart Computing, SMARTCOMP 2025
DOI	http://dx.doi.org/10.1109/SMARTCOMP65954.2025.00114
Abstract	Urban traffic is one of the most important issues for smart cities, and real-time management is critical for improving mobility and reducing congestion. Traditionally, classification methods based on machine learning need frequent retraining, which is inefficient for adaptive traffic management systems. This work introduces a novel hybrid approach that combines Incremental Learning (IL) with Federated Learning (FL) techniques to support continuous model adaptation without centralizing data or restarting the training process from scratch. The proposed approach employs Convolutional Neural Networks (CNNs) to classify traffic conditions from junction camera feeds and includes a specific mechanism to mitigate the Catastrophic Forgetting (CF) issue, a common drawback in IL. This solution enhances model performance in a decentralized way while protecting data privacy and encouraging knowledge sharing across distributed nodes. Experimental results on a publicly available dataset reveal that this approach significantly improves dynamic traffic management, achieving over 96% validation accuracy throughout the IL process for multiple clients, with minimal loss and no need to centralize data. This work sets the basis for a more effective and secure traffic management infrastructure in smart cities. © 2025 IEEE.
Author Keywords	Convolutional Neural Networks; Federated Learning; Incremental Learning; Urban Traffic Management

Similar Articles

Id	Similarity	Authors	Title	Published
44733	0.917	Karthick Raghunath K.M.; Rohith Bhat C.; Vinoth Kumar V.; Athiyoor Kannan V.; Mahesh T.R.; Manikandan K.; Krishnamoorthy N.	Redefining Urban Traffic Dynamics With Tcn-Fl Driven Traffic Prediction And Control Strategies	IEEE Access, 12 (2024)
60735	0.912	Kumari M.; Ulmas Z.; Suseendra R.; Ramesh J.V.N.; El-Ebiary Y.A.B., Prof.	Utilizing Federated Learning For Enhanced Real-Time Traffic Prediction In Smart Urban Environments	International Journal of Advanced Computer Science and Applications, 15, 2 (2024)
47595	0.891	Muhammad M.H.G.; Ahmad R.; Fatima A.; Mohammed A.S.; Raza M.A.; Khan M.A.	Secure And Transparent Traffic Congestion Control System For Smart City Using A Federated Learning Approach	International Journal of Advanced and Applied Sciences, 11, 7 (2024)
24076	0.891	Jiang Z.	Enhancing Urban Traffic Forecasting With Scalable, Privacy-Preserving Federated Learning Approach	2024 4th International Conference on Electronic Information Engineering and Computer Technology, EIECT 2024 (2024)
17679	0.886	Mohankumar C.E.; Manikandan A.	Decentralized Traffic Management With Federated Edge Ai: A Reinforced Transnet Model For Real-Time Vehicle Object Detection And Collaborative Route Optimization	Discover Applied Sciences, 7, 7 (2025)
12359	0.884	Sharma V.; Seetharaman T.; Bd V.; Khangaonkar A.M.	Blockchain And Federated Learning Enabled Smart Traffic Management System For Smart Cities	4th International Conference on Intelligent Engineering and Management, ICIEM 2023 (2023)
26403	0.883	Yuan X.; Chen J.; Yang J.; Zhang N.; Yang T.; Han T.; Taherkordi A.	Fedstn: Graph Representation Driven Federated Learning For Edge Computing Enabled Urban Traffic Flow Prediction	IEEE Transactions on Intelligent Transportation Systems, 24, 8 (2023)
7046	0.882	Rathore S.P.S.; Farhaoui Y.; Aniebonam E.E.; Nagpal T.; Thanuja M.; Kaushik P.	Ai-Driven Traffic Congestion Management: A Predictive Analytics Approach For Smart Cities	2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2025 (2025)
51592	0.881	Pritha A.; Fathima G.	Smart Traffic Management: A Deep Learning Revolution In Traffic Prediction - A Review	IET Conference Proceedings, 2024, 23 (2024)
58620	0.881	Almukhalfi H.; Noor A.; Noor T.H.	Traffic Management Approaches Using Machine Learning And Deep Learning Techniques: A Survey	Engineering Applications of Artificial Intelligence, 133 (2024)