CNN-BiGRU-Attention: A Time Series-Based Traffic Flow Prediction Model

At present, the task of traffic flow forecasting poses significant challenges. Existing studies predominantly utilize spatial characteristics for comprehensive forecasting and do not extensively consider the issue of extracting features from individual time series data. In the absence of known road topological structures, extracting effective temporal features of a single node becomes especially important. This paper proposes a new time series-based traffic prediction model-CNN-BiGRU-Attention(CBGA), which combines Convolutional Neural Networks (CNN), Bidirectional Gated Recurrent Units (BiGRU), and an attention mechanism. The model captures local features through CNN, captures long-term temporal dependencies through BiGRU, and introduces an attention mechanism to focus on key time steps, thereby effectively improving the forecasting performance. We conducted experiments using this model and compared it with traditional and modern models to demonstrate that the CBGA model has good performance in time series forecasting tasks and can effectively perform time series-based traffic flow forecasting tasks.