In order to enhance the accuracy of road traffic flow prediction, this paper proposes a traffic flow prediction model based on the self-attention mechanism TCN-BiGRU. Firstly, the predictive model utilizes the convolutional property of time convolutional network (TCN) to extract temporal correlations within traffic flow data across different time steps. Secondly, bidirectional BiGRU is employed to comprehensively capture time-related characteristics of traffic flow by updating and resetting gates. Recognizing that bidirectional gated recurrent unit has limited parallelism and may not capture certain features during bidirectional calculation, the introduction of self-attention mechanism allows the model to focus on global correlation between different inputs, overcoming limitations posed by sequence length in feature capture and maximizing feature retention for improved robustness. Finally, predicted values for traffic flow are obtained. To validate the applicability of the model, real traffic data is selected for multiple prediction and comparison experiments against benchmark models and ablation experimental models across various road segments. The results demonstrate that single or multiple section predictions using multiple features based on self-attention mechanism TCN-BiGRU yield mean MAE values of 15.91 and 19.62 respectively; MAPE/% values of 10.89 and 13.53 respectively; as well as R2 values of 0.976 and 0.982 respectively-indicating strong predictive performance.