Smart City Gnosys

Smart city article details

Title	Decentralized Traffic Management With Federated Edge Ai: A Reinforced Transnet Model For Real-Time Vehicle Object Detection And Collaborative Route Optimization
ID_Doc	17679
Authors	Mohankumar C.E.; Manikandan A.
Year	2025
Published	Discover Applied Sciences, 7, 7
DOI	http://dx.doi.org/10.1007/s42452-025-07383-6
Abstract	Urbanization and rising vehicle density have introduced significant challenges to traditional traffic management systems, including high latency, limited scalability, and concerns over data privacy. This study proposes a solution to these issues by developing a decentralized traffic management framework, DST-FedRL, which integrates Federated Edge AI, Deep Spatial-Temporal Transformer Networks (DST-net), and Reinforcement Learning. The aim is to enhance real-time vehicle detection, optimize traffic routes, and alleviate congestion while ensuring the privacy of sensitive data. The proposed system processes traffic data locally on edge devices, minimizing the need for data transfer and preserving privacy. The framework leverages Python-based tools, TensorFlow for model training, and reinforcement learning libraries for optimizing traffic routes. Federated learning is utilized to allow collaborative model updates across devices without the need to share raw data, while DST-net enables accurate vehicle detection under diverse and challenging conditions. The system was trained using the Road Traffic Video Monitoring Dataset, which includes a variety of traffic scenarios to ensure robust performance. The results show that DST-FedRL outperforms traditional methods, achieving a 97.8% vehicle detection accuracy, a 30% reduction in travel time, and a 27.9% decrease in fuel consumption. This study demonstrates that DST-FedRL is an effective, scalable, and privacy-preserving solution for intelligent traffic management in smart cities, offering significant improvements in efficiency and sustainability for urban mobility systems. © The Author(s) 2025.
Author Keywords	Edge AI; Federated learning (FL); Reinforcement learning (RL); Route optimization; Traffic management; Traffic object detection

Similar Articles

Id	Similarity	Authors	Title	Published
17678	0.886	Ficili I.; Tricomi G.; Cicceri G.; Longo F.; Vitabile S.; Puliafito A.	Decentralized Traffic Management Through A Hybrid Incremental And Federated Learning Approach	Proceedings - 2025 IEEE International Conference on Smart Computing, SMARTCOMP 2025 (2025)
26403	0.886	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)
24076	0.879	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)
44733	0.878	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)
5789	0.875	Ranka S.; Rangarajan A.; Elefteriadou L.; Srinivasan S.; Poasadas E.; Hoffman D.; Ponnulari R.; Dilmore J.; Byron T.	A Vision Of Smart Traffic Infrastructure For Traditional, Connected, And Autonomous Vehicles	Proceedings - 2020 International Conference on Connected and Autonomous Driving, MetroCAD 2020 (2020)
26321	0.874	Djenouri Y.; Michalak T.P.; Lin J.C.-W.	Federated Deep Learning For Smart City Edge-Based Applications	Future Generation Computer Systems, 147 (2023)
7021	0.872	Qaffas A.A.	Ai-Driven Distributed Iot Communication Architecture For Smart City Traffic Optimization	Journal of Supercomputing, 81, 8 (2025)
60735	0.869	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	0.868	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)
21807	0.868	Skadins A.; Ivanovs M.; Rava R.; Nesenbergs K.	Edge Pre-Processing Of Traffic Surveillance Video For Bandwidth And Privacy Optimization In Smart Cities	Proceedings of the Biennial Baltic Electronics Conference, BEC, 2020-October (2020)