## 5 Results and Analysis
### Ablation Studies  
Three independent ablation studies were conducted to research the crucial components of CeT. Apart from the studied components, the variants in these ablation studies share the same experiment setups with the complete model. Their experimental result is compared with that of the complete model to analyze the contributions of each component. To investigate the contribution of vehicle-state embeddings, two variants are designed based on the complete model:  
* noVSE variant--VSE is deleted from the complete model to research the impacts of the embedding structure. * noVSA variant--The vehicle-state attributes in VSE are removed to study the impacts of the additional information. Table 2 presents a detailed comparison of the performance of different model variants in two scenarios, focusing on the impact of VSE and VSA. The table evaluates the models using three metrics: MAE, RMSE, and ACC. In Scenario 1, the complete model with both VSE and VSA outperforms the variants that lack these components. Specifically, the CeT model achieves the lowest MAE of 0.247, the lowest RMSE of 0.565, and the highest accuracy of 84.81%. This indicates that incorporating both VSE and VSA significantly enhances the model's performance in this scenario. Similarly, in Scenario 2, the complete model again demonstrates superior performance with an MAE of 0.272, RMSE of 0.512, and accuracy of 84.36%. These results suggest that the inclusion of both vehicle-state embeddings and attention is beneficial across different scenarios, consistently leading to improved predictive accuracy and reduced error compared to models that exclude either the noVSE variant or noVSA variant. Figure 7 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 worst predictive performance of the noVSE variant can be attributed to the lack of vehicle-type embeddings. This deficiency makes it difficult for the noVSE variant to consider the differences among various types of vehicles in prediction. In comparison, the noVSA variant applies the same multi-head attention as the complete model to extract vehicle-state features, which significantly enhances predictive performance. Different from these variants, the complete model embeds the vehicle-type attributes into vehicle-state features and further improves the predictive performance, especially the initial states in the predicted result. To research the impact of temporal and spatial feature extraction on prediction, two variants are constructed based on the complete model: \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **Metric** & **noVSE** & **noVSA** & **CeT** \\ \hline \multirow{3}{*}{Scenario 1} & MAE & 0.272 & 0.263 & **0.247** \\  & RMSE & 0.628 & 0.601 & **0.565** \\  & ACC & 82.92 & 83.32 & **84.81** \\ \hline \multirow{3}{*}{Scenario 2} & MAE & 0.283 & 0.279 & **0.272** \\  & RMSE & 0.543 & 0.533 & **0.512** \\ \cline{1-1}  & ACC & 83.11 & 83.52 & **84.36** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation study for vehicle-state embeddings in Scenario 1 and Scenario 2. The best model is shown in bold font. noTSA variant--TSA in both the encoder and decoder are removed from the complete model to study their function. * noSSA variant--SSA in both the encoder and decoder are removed from the complete model to test their effectiveness. Table 3 provides a detailed comparison of the performance of different model variants in two scenarios, focusing on the impact of TSA and SSA. In Scenario 1, the complete model with both TSA and SSA outperforms the variants lacking 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 indicates that incorporating both types of self-attention significantly enhances the model's performance in this scenario. Similarly, in Scenario 2, the complete model demonstrates superior performance with an MAE of 0.272, RMSE of 0.512, and accuracy of 84.36%.