## 4 Comparison Analysis of Prediction Models
### Training Convergence and Feature Importance Interpretation Analysis  
The error decline curves of the models provide further insights into their convergence behaviors during training. Figure 9 shows that the BP model's error decreases rapidly between 0 and 200 iterations. It flattens out after 600 iterations, indicating that the model quickly minimizes its error initially but requires more iterations to fine-tune the weights for further performance improvements. Similarly, the GA-BP model's curve drops quickly \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model Type** & **Algorithmic Error** & **Accuracy** & **Recall** & **Precision** & **F1** & **Cost** \\ \hline BP Neural Network & 10.753 & 0.731 & 0.730 & 0.771 & 0.75 & 54 \\ GA-BP Neural Network & 11.038 & 0.791 & 0.757 & 0.848 & 0.8 & 46 \\ XGBoost & 0.21 & 0.720 & 0.716 & 0.756 & 0.74 & 55 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation Results of BP Model and GA-BP Model. because the genetic algorithm optimizes the weights and thresholds, accelerating the network's convergence speed. This rapid decrease in error indicates that even minor adjustments during the early stages of training can bring significant improvements. As the training progresses, the model gradually fine-tunes the weights, and the improvements become less dramatic as it approaches an optimal solution. In contrast, the XGBoost model's error decreases rapidly during the 0-50 iterations, but the curve tends to flatten out after 50 iterations, with an error value of 0.21 at this point. This sharp initial decline is followed by a plateau, indicating that the model quickly converges to a solution. However, further improvements are limited due to its loss function and optimization approach. The Error Decline Curve Chart of XGBoost is presented in Figure 10.  
It is worth noting that the error values are not directly comparable due to the difference in loss functions used by BP and GA-BP (Cross-Entropy Loss) and XGBoost (Taylor expansion). However, the number of iterations at which the error values stabilize can be compared. In this regard, XGBoost's error curve stabilizes at around 50 iterations, whereas BP and GA-BP show noticeable improvements well beyond this point, particularly at around 200 and 160 iterations, respectively. This difference highlights the distinct convergence characteristics of each model and their corresponding optimization strategies. Figure 9: Error Decline Curve Chart of BP Model and GA-BP Model. Figure 11 shows the feature importance analysis for three models: BP neural network, GA-BP neural network, and XGBoost. The feature importance values clearly illustrate how each model prioritizes different features during decision-making. Overall, the BP and GA-BP models show a similar feature importance distribution. STV and OTV are the most influential features, followed by SV and OV, indicating that traffic volume and vehicle speeds are key factors driving the prediction results for these two models. Figure 11: _Cont._  
Figure 10: Error Decline Curve Chart of XGBoost Model. Specifically, the BP model is highly important to STV and OTV, with values of 85 and 80, respectively. In contrast, the GA-BP model gives even higher importance to these features, with values of 90 and 82. These results confirm that these two features play a dominant role in the decision-making process for both models. In comparison, the XGBoost model assigns importance values of 81 and 76 to STV and OTV, which, while still high, are slightly lower than those of BP and GA-BP. This indicates that XGBoost places less emphasis on traffic volume than the BP and GA-BP models. SV consistently ranks as an important feature in all three models. The GA-BP model, in particular, assigns SV an importance value of 77, the highest among all features. The BP model values SV at 74, while XGBoost assigns it a value of 67. These values indicate that both BP and GA-BP models rely more heavily on SV than XGBoost, although SV also remains an important factor for XGBoost.