October 2, 2025PeerJ Computer ScienceOpen Access

Enhanced congestion prediction of traffic flow using a hybrid attention-based deep learning model

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Novel deep learning approach enhances accuracy of traffic congestion predictions in smart cities, suggesting AI can optimize urban transport systems.

The hybrid model significantly reduces traffic congestion prediction errors, leading to more efficient urban transport.
In Junction 1, the proposed model recorded the lowest RMSE of 0.252, demonstrating superior performance over traditional methods.
Using a combination of Bi-LSTM, CNN, and attention networks, the approach leverages various traffic data for prediction accuracy.
This method highlights the potential of AI in refining traffic management strategies within dynamic urban settings.

Rua Yahya Aburasain (2025) studied this question.

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Novel deep learning approach enhances accuracy of traffic congestion predictions in smart cities, suggesting AI can optimize urban transport systems.