## 5 Results and Analysis
### Ablation Studies
Figure 8 illustrates a detailed comparison of the MAE, RMSE, and ACC metrics at each time step within the predictive horizon for the complete model and its variants. The experimental results demonstrate that the predictive performance of the noTSA variant is better than that of the noSSA variant but worse than that of the complete model. The experimental results confirm the significance of both TSA and SSA and indicate the greater contribution of SSA to enhancing predictive accuracy. To investigate the guidance of temporal and spatial embeddings to prediction, three variants of the complete model are conducted as follows:  
Figure 7: Ablation study for vehicle-state embeddings in Scenario 1 and Scenario 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **Metric** & **noTSA** & **noSSA** & **CeT** \\ \hline \multirow{3}{*}{Scenario 1} & MAE & 0.256 & 0.271 & **0.247** \\  & RMSE & 0.592 & 0.626 & **0.565** \\  & ACC & 84.07 & 82.69 & **84.81** \\ \hline \multirow{3}{*}{Scenario 2} & MAE & 0.276 & 0.284 & **0.272** \\  & RMSE & 0.531 & 0.558 & **0.512** \\  & ACC & 83.53 & 82.67 & **84.36** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation study for temporal and spatial self-attention in Scenario 1 and Scenario 2. The best model is shown in bold font. * noSTE variant--SE and TE are deleted from the complete model to study their contribution. * noTE variant--TE are removed from the complete model to study their contribution. * noSE variant--The dynamic directed graph in SE is replaced by a static graph to study their contribution. Table 4 compares the prediction performance of different variants in two scenarios. In Scenario 1, the complete model with both TE and SE outperforms the variants that lack these components. Specifically, CeT achieves the lowest MAE of 0.247, the lowest RMSE of 0.565, and the highest accuracy of 84.81%. This demonstrates that the integration of both TE and SE significantly enhances the model's performance in this scenario. Similarly, in Scenario 2, the complete model shows superior performance with an MAE of 0.272, RMSE of 0.512, and accuracy of 84.36%. These results indicate that the inclusion of both TE and SE is beneficial across different scenarios, consistently leading to improved predictive accuracy and reduced error compared to models that exclude either noSTE, noTE, or noSE. Figure 9 presents the step-by-step comparison between the complete model and variants. The result of the noSTE variant exhibits the worst predictive performance, which confirms the contribution of STEs. In contrast, the predictive performance of the noSE variant is slightly better than that of the noSTE variant and remarkably worse than that of the complete model. This phenomenon demonstrates the effective assistance of SEs in enabling CeT to comprehend signal variation and guide predictions across different phases. Surprisingly, the predictive performance of the noTE variant is significantly better than that of the noSE variant but only slightly less than that of the complete model. One possible explanation is that the data in pNEUMA is collected during the morning peak hours, which exhibit similar temporal patterns. Figure 8: Ablation study for spatial-temporal embeddings in Scenario 1 and Scenario 2.