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This article discusses the use of deep learning techniques to improve the accuracy of traffic prediction in urban areas. By analyzing historical traffic data and considering variables such as speed, traffic volume, and direction, the model developed in this study showed better results compared to previous models. This has the potential to enhance the effectiveness of intelligent transportation systems and improve safety and energy efficiency in large cities.
