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Fusion: Practice and Applications

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Fusion: Practice and Applications

Volume 19 , Issue 2 , PP: 328-340, 2025 | Cite this article as | XML | Html | PDF | Full Length Article

Fusion of Real-Time Traffic and Environmental Sensor Data with Machine Learning for Optimizing Smart City Operations

Harish Reddy Gantla 1 , Sunil Kr Pandey 2 , Shailaja Mantha 3 , Priya Goyal 4 , Asmath Jabeen 5 , Shameem Fatima 6 , Udit Mamodiya 7 *

  • 1 Associate Professor, Department of Computer Science and Engineering, Vignan Institute of Technology and Science, Hyderabad, India - (harsha.rex@gmail.com)
  • 2 Professor, Department of Information Technology, Institute of Technology & Science, Ghaziabad, Uttar Pradesh, India - (sunil_pandey_97@yahoo.com)
  • 3 Associate Professor, Department of Electronics and Computer Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, India - (shailaja.mantha@gmail.com)
  • 4 Assistant Professor, Department of CSE (AI & ML), Manipal University Jaipur, Dahmi-Kalan, Jaipur, Rajasthan, India - (priyagupta2k20@gmail.com)
  • 5 Assistant Professor, Department of CSE (AI & ML), Nawab Shah Alam Khan College of Engineering and Technology, Hyderabad, India - (jasmath1786@gmail.com)
  • 6 Associate Professor, Department of Computer Science, Nawab Shah Alam Khan College of Engineering and Technology, Telangana, India - (nsakfatima@gmail.com)
  • 7 Associate Professor, Faculty of Engineering & Technology, Poornima University, Jaipur, India - (assoc.dean_research@poornima.edu.in)

    • Doi: https://doi.org/10.54216/FPA.190224

    Received: January 17, 2025 Revised: February 17, 2025 Accepted: March 08, 2025
    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.

    Keywords :

    Smart City Optimization , Real-Time Sensor Fusion , Machine Learning for Urban Management , Edge Computing in IoT , Blockchain-Based Data Security

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    Cite This Article As :
    Reddy, Harish. , Kr, Sunil. , Mantha, Shailaja. , Goyal, Priya. , Jabeen, Asmath. , Fatima, Shameem. , Mamodiya, Udit. Fusion of Real-Time Traffic and Environmental Sensor Data with Machine Learning for Optimizing Smart City Operations. Fusion: Practice and Applications, vol. , no. , 2025, pp. 328-340. DOI: https://doi.org/10.54216/FPA.190224
    Reddy, H. Kr, S. Mantha, S. Goyal, P. Jabeen, A. Fatima, S. Mamodiya, U. (2025). Fusion of Real-Time Traffic and Environmental Sensor Data with Machine Learning for Optimizing Smart City Operations. Fusion: Practice and Applications, (), 328-340. DOI: https://doi.org/10.54216/FPA.190224
    Reddy, Harish. Kr, Sunil. Mantha, Shailaja. Goyal, Priya. Jabeen, Asmath. Fatima, Shameem. Mamodiya, Udit. Fusion of Real-Time Traffic and Environmental Sensor Data with Machine Learning for Optimizing Smart City Operations. Fusion: Practice and Applications , no. (2025): 328-340. DOI: https://doi.org/10.54216/FPA.190224
    Reddy, H. , Kr, S. , Mantha, S. , Goyal, P. , Jabeen, A. , Fatima, S. , Mamodiya, U. (2025) . Fusion of Real-Time Traffic and Environmental Sensor Data with Machine Learning for Optimizing Smart City Operations. Fusion: Practice and Applications , () , 328-340 . DOI: https://doi.org/10.54216/FPA.190224
    Reddy H. , Kr S. , Mantha S. , Goyal P. , Jabeen A. , Fatima S. , Mamodiya U. [2025]. Fusion of Real-Time Traffic and Environmental Sensor Data with Machine Learning for Optimizing Smart City Operations. Fusion: Practice and Applications. (): 328-340. DOI: https://doi.org/10.54216/FPA.190224
    Reddy, H. Kr, S. Mantha, S. Goyal, P. Jabeen, A. Fatima, S. Mamodiya, U. "Fusion of Real-Time Traffic and Environmental Sensor Data with Machine Learning for Optimizing Smart City Operations," Fusion: Practice and Applications, vol. , no. , pp. 328-340, 2025. DOI: https://doi.org/10.54216/FPA.190224

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