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Smart city article details

Title	Innovations To Enhance Traffic Prediction And Empowering Iov For Smart Cities
ID_Doc	31654
Authors	Negi S.K.; Sharma S.; Chandra P.K.
Year	2025
Published	ESIC 2025 - 5th International Conference on Emerging Systems and Intelligent Computing, Proceedings
DOI	http://dx.doi.org/10.1109/ESIC64052.2025.10962682
Abstract	Predicting traffic flow is important for improving safety and efficiency in IoV systems. Techniques such as traditional models, deep learning, hybrid approaches, graph-based methods, optimization, edge computing, and feature fusion techniques are examined in this study. Hybrid techniques and Edge computing models reached the highest accuracy of 98.5% and 98.06%, respectively, with minimal error MAE: 4.9, RMSE: 9.8. Deep learning and feature fusion performed well. At the same time, traditional ML and optimization methods lagged. Challenges such as computational complexity and data quality persist. Future work should be on lightweight, scalable, and privacy-preserving models using 5G, IoT, and blockchain for smarter transportation networks. © 2025 IEEE.
Author Keywords	ARIMA; CNN; GoW; IoV; LSTM

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