Traffic flow prediction with a multi-dimensional feature input: A new method based on attention mechanisms

Accurately predicting traffic flow is an essential component of intelligent transportation systems. The advancements in traffic data collection technology have broadened the range of features that affect and represent traffic flow variations. However, solely inputting gathered features into the model without analysis might overlook valuable information, hindering the improvement of predictive performance. Furthermore, intricate dynamic relationships among various feature inputs could constrain the model's potential for further enhancement in predictive accuracy. Consequently, extracting pertinent features from datasets and modeling their mutual influence is critical in attaining heightened precision in traffic flow predictions. First, we perform effective feature extraction by considering the temporal dimension and inherent operating rules of traffic flow, culminating in Multivariate Time Series (MTS) data used as input for the model. Then, an attention mechanism is proposed based on the MTS input data. This mechanism assists the model in selecting pertinent time series for multivariate forecasting, mitigating inter-feature influence, and achieving accurate predictions through the concentration on crucial information. Finally, empirical findings from real highway datasets illustrate the enhancement of predictive accuracy attributed to the proposed features within the model. In contrast to conventional machine learning or attention-based deep learning models, the proposed attention mechanism in this study demonstrates superior accuracy and stability in MTS-based traffic flow prediction tasks.