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Title	Smart Traffic Management: A Deep Learning Revolution In Traffic Prediction - A Review
ID_Doc	51592
Authors	Pritha A.; Fathima G.
Year	2024
Published	IET Conference Proceedings, 2024, 23
DOI	http://dx.doi.org/10.1049/icp.2024.4432
Abstract	In the era of smart cities and autonomous vehicles, efficient traffic management is critical to reducing congestion, enhancing safety, and improving overall urban mobility. Traditional traffic prediction models, while effective to a certain extent, often fall short in handling the complexities of modern traffic patterns influenced by a multitude of factors such as weather, events, and fluctuating demand. This paper presents a novel approach to traffic prediction leveraging the power of deep learning. Deep Learning, a subset of machine learning, excels in identifying intricate patterns within large datasets. By employing advanced neural network architectures such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs), captures both temporal and spatial dependencies in traffic data. The model is trained on extensive historical traffic datasets, integrating additional contextual information such as meteorological data and public event schedules to enhance predictive accuracy. Deep learning models demonstrate traditional statistical methods like ARIMA in forecasting traffic volume and congestion levels. The results reveal an improvement in prediction accuracy, particularly sudden traffic surges and mitigating the impact of unforeseen disruptions. The potential of deep learning revolutionizes traffic prediction, paving smarter, more responsive urban traffic control. © The Institution of Engineering & Technology 2024.
Author Keywords	Deep Learning; Neural Networks; Predictive Modeling; Smart city solutions; Traffic Forecasting

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