## 4 Experiment
### Experimental Results  
The CPO-BiTCN-BiLSTM-SA model proposed in this chapter uses a CPO algorithm to optimize the BiTCN-BiLSTM-SA network to obtain the best network parameters. Figure 11 shows the optimal fitness change curve of C-BTLS in Dataset A. It can be seen that the CPO algorithm reaches the optimal solution when it is iterated to the 9 th, 6 th, and 6 th generation in the 1-step, 2-step, and 3-step flow predictions, respectively. On this basis, the best parameters of the BiTCN-BiLSTM-SA network in Dataset A were obtained, as shown in Table 2.  
Figure 10: Simulated traffic segment. Figure 12: Plot of the single-step prediction results for Dataset A.  
Figure 13: Plot of the two-step prediction results for Dataset A. In order to show the performance enhancement of the proposed traffic model more clearly, XGBoost was selected for the purposes of plotting and comparison, of which the performance on both datasets is shown in Figures 12-14 (where each color denotes the predicted or actual values for both the datasets). When combining the information from Table 3 and Figures 12-14, it becomes evident that CPO-BiTCN-BiLSTM-SA outperforms the other 8 models. Moreover, from Table 3, it can be observed that the BiTCN-BiLSTM-SA model exhibits superior prediction performance compared to the BiTCN-BiLSTM model. Taking Dataset A as an example, the RMSE decreased by 2.52%, one step; 6.35%, two steps; and 3.00%, three steps. The MAE decreased by 5.92%, one step; 4.10%, two steps; and 2.27%, three steps. The MAPE decreased by 8.92%, one step; 6.10%, two steps; and 4.09%, three steps. The R\({}^{2}\) was elevated by 1.49%, one step; 0.60%, two steps; and 0.21%, three steps. These results indicate that the SA mechanism effectively enhances the performance by prioritizing the crucial information during training. Furthermore, when contrasting the BiTCN-BiLSTM model to the TCN-LSTM model, it is evident that the bidirectional structure excels in extracting profound insights compared to the unidirectional structure. Specifically, considering Dataset A, the RMSE, MAE, and MAPE decreased by 6.10%, 14.96%, 6.35%, respectively, for one step; by 8.04%, 5.15%, 13.50%, respectively, for two step; and by 15.35%, 5.90%,2.20%, respectively, for three step. The R\({}^{2}\) increased by 2.17%, one step; 1.10%, two steps; and 2.05%, three steps. These findings underscore the efficacy of bidirectional structures in extracting comprehensive information for improved prediction outcomes. Figures 15 and 16 depict the evaluation metrics of each model for Dataset A and Dataset B. It is evident that the BiTCN-BiLSTM model is superior to TCN-LSTM model, primarily due to its bidirectional structure, as well as it facilitation of bidirectional feature extraction. The causally inflated convolutional structure of BiTCN enables the model to capture information over a broader time range owing to its larger field of view. Meanwhile, the BiLSTM integrates forward and backward LSTMs, enhancing the model's capacity to retain long-term information and grasp the dynamics of data from both temporal directions. Moreover, the SA layer, positioned after the BiTCN-BiLSTM layer, leverages the self-attention mechanism to capture the global dependencies in time series data. This enables the model to focus on the critical segments of the series, thus fostering enhanced feature interactions and facilitating the learning of complex and abstract features. Utilizing CPO for hyperparameter optimization in the BiTCN-BiLSTM-SA model yields superior results across all evaluation criteria compared to manually defined hyperparameter configurations. For instance, the CPO-BiTCN-BiLSTM-SA model reduces the RMSE, MAE, and MAPE by 11.71%, 4.13%, and 3.71%, respectively, while enhancing \(R^{2}\) by 0.82% ov  
Figure 14: Plot of the single-step prediction results for Dataset B.  
model for single-step prediction on Dataset A. This underscores the effectiveness of CPO in enhancing the model prediction accuracy across various evaluation metrics.