Enhancing Traffic Flow Prediction with an Attention Mechanism in a Deep Learning Model

Traffic congestion has emerged as a critical global challenge, impacting daily life, fuel consumption, and environmental sustainability. Accurate traffic flow prediction is crucial for developing advanced transportation systems. While existing methods leveraging deep learning, such as Conv-LSTM networks, have made significant strides, there is potential for improvement in extracting and utilizing temporal and spatial dependencies more effectively. This study introduces a Conv-LSTM model enhanced with an attention mechanism, enabling the model to allocate varying importance to traffic flow sequences and extract deeper spatiotemporal features. Furthermore, a comparative evaluation of various attention mechanisms from the literature highlights the effectiveness of the proposed approach. Experimental results on real-world datasets demonstrate that the integration of attention layers significantly enhances the model's predictive performance, particularly for short-term traffic forecasting.