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Title	Intelligent Traffic Prediction System Using Hybrid Convolutional Neural Networks For Smart Cities
ID_Doc	32617
Authors	J J.S.; G A.K.; kumar E.; Raju K.N.; Sudha V.; Kshirsagar P.R.; Tirth V.; Rajaram A.
Year	2024
Published	Multimedia Tools and Applications
DOI	http://dx.doi.org/10.1007/s11042-024-20420-7
Abstract	Intelligent Traffic Management Systems (ITMS) play a crucial role in advancing smart city applications by addressing the rapid increase in road traffic due to the growing number of vehicles. Effective real-time traffic prediction is essential to avoid congestion, yet existing systems often struggle with high delays and insufficient accuracy. This paper introduces an innovative ITMS based on a Hybrid Convolutional Neural Network (HCNN) to overcome these challenges. The proposed system integrates numerous smart devices, including smart vehicles, with an edge computing framework where the HCNN model is deployed. The HCNN combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enhance prediction accuracy and reduce latency. Experimental results demonstrate that the HCNN model achieves up to 95% prediction accuracy while reducing delays by up to 50% compared to conventional methods. These improvements are significant for smart city applications, ensuring timely and precise traffic management. The study validates the effectiveness of the HCNN model in improving traffic prediction and proposes its potential for broader smart city implementation. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Author Keywords	CNN; Deep learning; ITMS; Smart City Applications; Traffic prediction

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