Deep Learning Approach with Attention Mechanism for Multivariate Mixed Traffic Flow Prediction

Traffic congestion is a major challenge in urban areas, primarily driven by industrialization, urbanization, and population growth. Governments and transportation agencies worldwide are actively seeking effective solutions to mitigate this issue. This study addresses traffic prediction under mixed traffic conditions by categorizing vehicles, which is essential for understanding and managing heterogeneous traffic flows. We propose a predictive model that forecasts traffic in terms of categorized vehicle speed and volume. The model integrates a Convolutional Long Short-Term Memory (ConvLSTM) neural network with an LSTM-based attention mechanism to enhance spatiotemporal learning capabilities. It is trained on seven multivariate features, including categorized vehicular speeds, time of day, and day of the week, using the same set of features as input for forecasting. The performance of the proposed model is evaluated against various baseline neural network architectures, including Dense, Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and ConvLSTM. Experimental results demonstrate that the proposed model outperforms these baselines in terms of prediction accuracy and stability. In particular, compared with the ConvLSTM model, the proposed approach achieves improvements of 48.06% in mean squared error (MSE), 14.40% in mean absolute error (MAE), and 14.51% in mean pinball loss (MPL), demonstrating the effectiveness of incorporating the attention mechanism for improved traffic forecasting under mixed traffic conditions.