This research paper delves into the pivotal role of deep learning in advancing traffic prediction techniques. With urban traffic management becoming increasingly intricate, accurate short-term traffic prediction remains a cornerstone for effective congestion mitigation and transportation planning. Leveraging the capabilities of deep learning methodologies, this study systematically explores various deep learning models and their applications in predicting traffic patterns. This investigation clarifies the advantages and disadvantages of deep learning approaches in traffic prediction by looking at current developments, techniques, and case examples. Moreover, it highlights avenues for further research and development to enhance the accuracy and applicability of deep learning-based traffic prediction systems, ultimately contributing to the evolution of intelligent transportation systems and the optimization of urban mobility. Examine some of the most recent developments in deep learning for traffic flow prediction. Convolutional neural networks (CNN), recurrent neural networks (RNNs), long short-term neural networks (LONG-SNNNs), Stacked Auto Encoder (SAE), Restricted Boltzmann Machines (RBM), and Term Memory (LSTM). These deep learning models gradually extract higher-level information from raw input by using numerous layers. Due to the complexity of transportation networks, the most recent deep learning models created to address this challenge are examined. The reader is also informed on how numerous aspects affect these models and which models perform best in specific circumstances.
