## 3 Simulation Experiments
### Comparative Analysis of Models  
When comparing models, we usually calculate the error to assess the predictive accuracy of the model [35]. Common error metrics include absolute and relative errors. Absolute error is the absolute value of the discrepancy between the forecasted and the actual measured value, which can measure the degree of discrepancy between the model's estimated output and the true observed data, and the smaller the value indicates that the model's prediction is more accurate. Relative error is the ratio between the absolute error and the actual observed value, relative error can reflect the relative accuracy of the model prediction, it will be compared with the actual observed value, and lower values imply a higher proximity of the model's predictions to the actual measured data. Both absolute and relative errors are commonly used metrics to assess the predictive accuracy of models, and by calculating these error metrics, we can compare the predictive performance of different models and select the best-performing model. 1. Linear regression: linear regression modeling is a simple but powerful statistical method whose basic goal is to find the optimal regression coefficients so that the difference between the predicted values of the model and the actual observations (i.e., residuals) is as small as possible. 2. Random Forest: a unified learning approach that enhances the precision and stability of forecasts through the creation of numerous decision trees. 3. RNN: its special feature is the connected structure of the network, which allows information to flow through time series data and can effectively encapsulate the temporal relationships within the sequential data. 4. GBDT: It minimizes the gradient of the loss function by iteratively constructing a decision tree to improve prediction accuracy. 5. LSTM [36]: It iteratively constructs a decision tree to minimize the gradient of the loss function to improve prediction accuracy. 6. STBiPGAT [37]: a spatiotemporal attention model that accurately captures the spatiotemporal dynamics of traffic flow. Using the correlation data associated with each road segment as the basic dataset, a variety of prediction methods were used to compare with the SVR model established in this paper, and the outcomes are depicted in Figure 14.  
As can be seen from Figure 14, by comparing the root mean square error, the mean absolute error, and the mean relative error of each model, it can be seen that the SVR model for the associated roads established in this paper performs better, and it is evident that the fitting effect of the SVR model is the best model among the compared models. Although the performance values of the STBiPGAT model are similar to those from the model introduced in this study, it fails to consider the influence of related road traffic. Confidence intervals are indispensable for a more comprehensive assessment of model performance, especially in multiple experiments. Confidence intervals can provide a range that indicates the probability that a model performance metric may fall within that range over multiple repetitive experiments. The results of a single experiment may be affected by the randomness of the dataset, differences in model initialization, hyperparameter selection, and other factors. By calculating confidence intervals, the uncertainty in model performance can be assessed more accurately, thus providing a more reliable performance assessment. In Figure 13, the performance of the SVR model is compared with other models (e.g., LSTM, Random Forest, etc.). By adding confidence intervals, performance differences between models can be more accurately assessed. If the confidence intervals of two models do not overlap, this indicates that the difference in performance between them is statistically significant; if the confidence intervals overlap, further analysis may be required to determine whether the difference is significant.