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...

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Bibliographische Detailangaben

Hauptverfasser:	Dalal, Surjeet, Jaglan, Vivek, Agrawal, Akshat, Kumar, Ajay, Joshi, Shashikant J., Dahiya, Mamta
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Optimization Prediction models Predictions Real time Recurrent neural networks Regularization Resource allocation Traffic congestion Transportation networks Transportation systems
Online-Zugang:	Volltext
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