## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Predictions after** \\ **n – 1 Time Step** \\ **(min)** \\ \end{tabular} } & \multicolumn{2}{c}{**STGCN (Speed, TSR, Brake)**} & \multicolumn{2}{c}{**STGCN (Speed) +**} \\  & \multicolumn{2}{c}{**Brake)**} & \multicolumn{2}{c}{**CNN (TSR, Brake)**} \\ \cline{2-7}  & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** \\ \hline
60 & 17.2089 & 21.009 & 17.1457 & 20.8469 & 17.0332 & 20.7712 \\
70 & 17.2459 & 20.9811 & 17.1490 & 20.9015 & 17.0503 & 20.7862 \\
80 & 17.1122 & 20.8314 & 17.2566 & 20.9147 & 17.1464 & 20.9856 \\
90 & 17.2394 & 20.9632 & 17.2427 & 20.9085 & 17.0812 & 20.8542 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Prediction results of MAE and RMSE. and red regions) are relatively sparse, suggesting that while the hybrid model performs well overall, there are specific regions where further improvements could be made. These high-error areas could be due to unique traffic patterns or insufficient data, which the hybrid model may not fully capture. To confirm whether the results of this experiment are statistically significant, we apply a one-way ANOVA test using MAE and RMSE values of each model. ANOVA testing can indicate whether there are any statistical differences between the means of three or more independent groups. Before conducting an ANOVA test, we assess the assumptions of the ANOVA test. In this study, we apply the Shapiro-Wilk [53] and the Levene tests [54] to assess normality and homoscedasticity. These tests presented that the data did not break the assumptions (\(p\)-value > 0.05). Finally, the ANOVA test revealed that there was a statistically significant difference among groups (\(F(2,\ 9)=6.09\), \(p\)-value < 0.05) for MAE. However, the ANOVA test for RMSE showed no statistically significant differences (\(F(2,\ 9)=1.68\), \(p\)-value = 0.23). Additionally, this study conducted post hoc tests for pairwise comparisons that can compare all different combinations of the treatment groups. [PERSON]'s Honestly Significant Difference (HSD) test revealed that the proposed model showed a statistically significant difference compared with the CNN model (\(p\)-value < 0.05). When compared to STGCN,  
Figure 8: Prediction error maps of road links between the predicted speed and the observed speed using MAE. there was also a statistically significant difference (\(p\)-value < 0.05). With regard to CNN and STGCN, \(p\)-value = 0.996, indicating that there was no statistically significant difference between CNN and STGCN. Furthermore, we calculated the effect size by dividing the average difference between the two sample groups by the estimated standard deviation. The effect size indicates the similarity between the distributions of the two groups. When the effect size is large, the overlap between the two groups is small. Therefore, the two groups are very different. When the effect size is small, the overlap between the two groups is significant, i.e., the two groups are very similar. The effect size indicates the extent of difference between the groups to be compared. The effect sizes, such as [PERSON]'s d, between the proposed model and STGCN model, and between the proposed model and CNN model, are 2.207 and 2.201, respectively. Both cases show very large effect sizes. The effect size indicates the practical degree of difference between groups. Therefore, the experimental results demonstrated that the proposed model outperformed alternative models, statistically and practically regarding MAE. In addition, [PERSON]'s d of RMSE between STGCN and our proposed model was 1.09, which indicates a large effect size. The effect size between CNN and the proposed model indicated a medium effect size (i.e., [PERSON]'s d = 0.52). Accordingly, although there are no statistically significant differences for RMSE, there are practical differences for RMSE. Regarding the effect of data, in this study, we investigated the effect of TSR data on traffic speed prediction.