Bidirectional Dual-Cell Recurrent Neural Networks for Traffic Flow Prediction

Traffic flow prediction is a key issue in urban traffic management, providing important support for urban planning and traffic diversion. In traffic flow prediction, LSTM (Long Short-Term Memory networks) and GRU (Gated Recurrent Units) capture temporal information with different focuses, but single-cell candidate states often lack full feature aggregation, limiting model performance. Challenges include long-term dependencies and large-scale data processing. This paper proposes an innovative dual-gating mechanism integrating LSTM and GRU candidate states to enhance accuracy. Experimental results show significant outperformance in Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), improving traffic flow predictions and urban traffic management.