Abstract
Urban traffic management systems are critical for reducing congestion, improving safety, and enhancing the overall transportation experience in cities. Deep learning techniques have emerged as powerful tools for optimizing real-time traffic flow, analyzing traffic patterns, and predicting congestion. This article explores the applications of deep learning in real-time traffic management, focusing on areas such as traffic prediction, adaptive traffic signal control, and vehicle tracking. By leveraging deep learning algorithms, cities can develop smarter, more efficient traffic systems that adapt dynamically to changing conditions and improve mobility for all road users.
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