Navigating urban congestion: Optimizing LSTM with RNN in traffic prediction

Urban congestion hinders transportation systems, requiring creative traffic forecasts. Using Recurrent Neural Networks (RNNs), this study optimizes LSTM networks for traffic prediction. Predictive models should be more accurate and adaptable. The hybrid LSTM-RNN architecture captures traffic data’s short-term patterns and long-term dependencies by combining LSTM memory cells and RNN sequential processing. Optimization methods include hybrid architectures, feature engineering, hyperparameter tuning, and regularization. Improved precision, long-term prediction, and resource allocation are highlighted in improved LSTM-RNN models. However, data accuracy, model comprehension, and computer process complexity must be addressed. Future directions include adding data sources, improving model designs, real-time adaptation, and interdisciplinary collaboration. LSTM with RNN traffic prediction can help manage urban congestion and shape transportation networks.