Smart City Gnosys
Smart city article details
| Title | Fusion Of Real-Time Traffic And Environmental Sensor Data With Machine Learning For Optimizing Smart City Operations |
|---|---|
| ID_Doc | 27466 |
| Authors | Gantla H.R.; Pandey S.K.; Mantha S.; Goyal P.; Jabeen A.; Fatima S.; Mamodiya U. |
| Year | 2025 |
| Published | Fusion: Practice and Applications, 19, 2 |
| DOI | http://dx.doi.org/10.54216/FPA.190224 |
| Abstract | The complex developing nature of urban infrastructure necessitates intelligent solutions for optimizing smart city operations. Based on this research paper, a multi-modal fusion framework that integrates real-time traffic and environmental sensor data with advanced machine learning algorithms to enhance decision-making for urban traffic management and pollution control is proposed. A hybrid AI model is proposed, with a combination of CNNs for the estimation of image-based traffic density, LSTM networks for the time-series environmental prediction, and RL for adaptive control of traffic signals. The system proposed integrates sensor data in real-time from cameras, GPS, LiDAR, and nodes for environmental monitoring to create an optimized control strategy. The model has been deployed on edge computing devices, such as Raspberry Pi, to enable the real-time processing and reduce the latency. Security layer based on block chain for data integrity protection and tamper proofing within smart city networks. The suggested system shows high improvements in congestion reduction, better accuracy in air pollution forecasting, and energy efficiency in urban management. It will be validated using simulation with SUMO and MATLAB and real-world sensor data that the sensor fusion approach outperforms the conventional fixed-rule strategies of traffic management. This work allows for cost-effective, large-scale smart city deployment that would reduce traffic delay and urban air pollution while securing data and being computationally efficient. The low-latency decision-making approach with edge-AI makes it fit for real-time urban governance. Unlike traditional models that process either traffic or environmental data in silos, the work presented herein integrates multi-source sensor data with edge computing and blockchain security for a unified AI-driven fusion approach, thus building a robust framework for next-generation smart city intelligence. © 2025, American Scientific Publishing Group (ASPG). All rights reserved. |
| Author Keywords | Blockchain-Based Data Security; Edge Computing in IoT; Machine Learning for Urban Management; Real-Time Sensor Fusion; Smart City Optimization |
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