A novel CNN-GRU-LSTM based deep learning model for accurate traffic prediction

Effective traffic prediction is crucial for optimizing urban transportation systems, minimizing congestion, and enhancing overall efficiency. Traffic congestion results in prolonged travel durations, higher fuel consumption, economic setbacks, and increased environmental pollution. To tackle these issues, we introduce a Hybrid CNN-GRU-LSTM model—an advanced deep learning framework that combines convolutional neural networks (CNN), gated recurrent units (GRU), and long short-term memory (LSTM) networks. This integrated model is specifically designed to capture both spatial and temporal patterns of traffic flow, making it highly effective for predicting vehicle volumes at intersections. The Hybrid CNN-GRU-LSTM leverages CNN to model spatial dependencies between road segments, while GRU and LSTM layers handle short-term and long-term temporal patterns in traffic data. This combination allows for more accurate predictions by incorporating spatial relationships and temporal dynamics simultaneously. The model was tested using publicly available datasets, including PeMS, the England dataset, the P/Castellano dataset, and the Fedesoriano dataset, and results demonstrate that Hybrid CNN-GRU-LSTM significantly outperforms several state-of-the-art models, achieving a reduction of up to 30–35% in error values. This study highlights the effectiveness of combining CNN, GRU, and LSTM architectures for traffic prediction, offering a robust solution for transportation management. The proposed model’s significant improvement in prediction accuracy can help mitigate the adverse effects of traffic congestion and enhance the overall performance of transportation networks.