## 5 Model Validation  
### Model Calibration  
Amos 28.0 software was employed to test the model paths and analyze the relationships between variables in the hospital traffic attraction model. The specific steps are as follows, as illustrated in Figure 5. First, data on hospital characteristics and transportation features are collected and standardized to eliminate dimensional differences. Next, based on the research hypotheses, a structural equation model (SEM) is established, and the Maximum Likelihood Estimation (MLE) method is used for model fitting to estimate path coefficients and factor loadings. The significance of the model parameters is then assessed through statistical tests, such as the T-test, to ensure the reliability of the results. Additionally, the overall model fit is evaluated using fit indices, such as CFI and TLI, to verify  
\begin{table}
\begin{tabular}{c c c c} \hline
**Fit Index** & **General Hospital** & **Specialized Hospital** & **Fit Criteria** \\ \hline CMIN/DF & 1.382 & 1.899 & 1–3 \\ GFI & 0.919 & 0.896 & \textgreater{}0.80 \\ CFI & 0.953 & 0.921 & \textgreater{}0.90 \\ RMSEA & 0.078 & 0.089 & \textless{}1.00 \\ \hline \end{tabular}
\end{table}
Table 4: Evaluation of model fit.  
its theoretical validity. Finally, the factor loadings and path coefficients are interpreted, and the relationships between latent variables (e.g., hospital size, traffic accessibility) and observed variables (e.g., building area, number of bus routes) are analyzed, revealing the mechanisms through which these factors influence hospital traffic attraction. The results showed that all three hypotheses were supported. Based on these findings, appropriate modifications were made to the model, including the reasonable association of variables. The final revised structural equation model for the factors influencing outpatient traffic attraction is shown in Figures 6 and 7.  
Based on the path relationships and parameter calibration results from the traffic attraction model diagram, the factor loadings of each observed variable and their relationships with latent variables can be obtained. The variable relationship between construction area and hospital size is calibrated according to Equation (5), and other variables can be calibrated in the same manner by following this example.  
\[x_{construction area}=0.49\%_{hospital Size}+0.24 \tag{7}\]  
Figure 5: Structural Equation Model calculation flowchart.  
Figure 6: Path relationship and parameter calibration results of the traffic attraction model for general hospitals.  
The impact relationships between model variables are determined by the sign of the standardized path coefficients, indicating the positive or negative correlation between the variables. The magnitude of the path coefficient's absolute value indicates the degree of influence between the two variables. A larger absolute value suggests that a change in one variable has a greater impact on the other variable in the path. The standardized path coefficients and hypothesis tests of the structural equation model are presented in Table 5. The results show that hospital size and medical level have a positive impact on the traffic attraction of general hospitals, while Hypothesis \(H_{3}\) is rejected, indicating that traffic accessibility has no effect on this. For specialized hospitals, Hypothesis \(H_{1}\) is rejected, while Hypotheses \(H_{2}\) and \(H_{3}\) are accepted, suggesting that the traffic attraction of specialty hospitals is significantly influenced by medical level and traffic accessibility.