## 3 Simulation Experiments
### Simulation of SVR Short-Time Traffic Flow Prediction Model Based on Associated Roads
#### 3.3.2 Analysis of SVR Short-Term Traffic Flow Prediction Model
In our results, the D-W test statistic values are close to 2 (1.7764 for the overall model, 1.7461 for the branch circuit model, and 1.7875 for the trunk road model), indicating that there is no significant autocorrelation in the residuals. This suggests that the model has effectively captured the underlying patterns in the data and that the residuals are randomly distributed. Error variance: Error variance is a measure of the variability of the prediction errors. It provides an indication of how much the predicted values deviate from the actual values. A lower error variance indicates that the model's predictions are closer to the actual values, suggesting better prediction accuracy. In our results, the error variance for the overall model is 35.142, indicating a relatively low level of variability in the prediction errors. The slightly higher error variance for the branch circuit model (37.621) compared to the trunk road model (34.278) reflects the slightly more complex traffic patterns on branch roads, which may be more challenging for the model to predict accurately. MAE (mean absolute error): MAE is a measure of the average magnitude of the errors in a set of predictions, without considering their direction. It is calculated as the average of the absolute differences between the predicted and actual values. A lower MAE indicates better prediction accuracy. In our results, the MAE for the overall model is 7.426, indicating that the average magnitude of the prediction errors is relatively small. The slightly higher MAE for the branch circuit model (7.511) compared to the trunk road model (7.386) suggests that the model's predictions for branch roads have slightly larger errors on average, which may be due to the more complex traffic patterns on branch roads. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **R\({}^{2}\)** & **D-W Tests** & **Error Variance** & **MAE** & **MRE** \\ \hline Overall model & 0.9261 & 1.7764 & 35.142 & 7.426 & 0.132 \\ Branch circuit model & 0.9127 & 1.7461 & 37.621 & 7.511 & 0.141 \\ Trunk road model & 0.9395 & 1.7875 & 34.278 & 7.386 & 0.123 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of road simulation results based on SVR model. MRE (mean relative error): MRE is a measure of the average relative magnitude of the errors in a set of predictions. It is calculated as the average of the absolute differences between the predicted and actual values, divided by the actual values. A lower MRE indicates better prediction accuracy relative to the actual values. In our results, the MRE for the overall model is 0.132, indicating that the average relative magnitude of the prediction errors is relatively small. The slightly higher MRE for the branch circuit model (0.141) compared to the trunk road model (0.123) suggests that the model's predictions for branch roads have slightly larger relative errors on average, which may be due to the more complex traffic patterns on branch roads. In summary, compared with the prediction results of trunk roads, the SVR prediction model of associated roads established in this paper improves the road prediction accuracy of branch roads with less traffic flow, and by analyzing the overall model, it is observed that the model developed in this study is capable of fulfilling the requirements for short-term traffic flow forecasting.