## 5 Results and Analysis
### Predictive Results  
Table 1 presents a comparison of predictions between CeT and baseline models for two-phase signalized intersections (Scenario 1) and four-phase signalized intersections (Scenario 2), as described in Section 4. The results demonstrate that the predictive performance of CeT outperforms all baselines in both Scenario 1 and Scenario 2. CeT enhances the best predictive performance in the baselines by 11.47%, 13.28%, and 4.36% in terms of MAE, RMSE, and ACC in Scenario 1. Similarly, CeT respectively improves MAE, RMSE, and ACC by 13.36%, 13.03%, and 4.78% in Scenario 2. Figure 6 compares the predictive performance of the CeT model with baseline models (FNN, AGCRN, GWN) in two scenarios. Across three metrics--MAE, RMSE, and ACC--over a predictive horizon of 0 to 20 s, the CeT model consistently outperforms the baseline models, demonstrating lower prediction errors (MAE and RMSE) and higher accuracy (ACC). Furthermore, the performance of the CeT model degrades significantly less as the predictive horizon increases, showcasing greater robustness and stability. As statistic methods, HA and ARIMA analyze historical traffic data at signalized intersections to identify periodic evolution patterns for future traffic state prediction. However, their neglect of previous traffic states hinders their ability to consider emergencies in fore \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Scenario** & **Metric** & **HA** & **ARIMA** & **RF** & **SVR** & **FNN** & **AGCRN** & **GWN** & **CeT** \\ \hline \multirow{3}{*}{Scenario 1} & MAE & 0.437 & 0.372 & 0.521 & 0.675 & 0.297 & 0.303 & 0.279 * & **0.247** \\  & RMSE & 1.093 & 0.792 & 1.136 & 1.334 & 0.685 & 0.704 & 0.653 * & **0.565** \\  & ACC & 61.13 & 67.54 & 74.51 & 73.07 & 77.21 & 78.49 & 81.27 * & **84.81** \\ \hline \multirow{3}{*}{Scenario 2} & MAE & 1.497 & 1.017 & 0.448 & 0.468 & 0.358 & 0.327 & 0.314 * & **0.272** \\  & RMSE & 2.186 & 1.429 & 0.864 & 0.933 & 0.671 & 0.632 & 0.588 * & **0.512** \\ \cline{1-1}  & ACC & 58.38 & 61.86 & 68.23 & 68.33 & 76.98 & 78.72 & 80.51 * & **84.36** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The prediction comparison between CeT and baseline models. The best result is highlighted in bold, and the second-best result is labeled with an asterisk. casting. Further, these statistic models exhibit poor predictive performance at multi-signal intersections. In contrast to HA and ARIMA, SVR and RF establish the traffic state space and train the state evolution model based on historical data. By successively updating the state of cells, these machine learning methods can forecast future traffic states. However, these methods fail to sufficiently utilize the spatial relationship among cells, resulting in inferior predictive performance in Scenario 2 compared to Scenario 1. Compared to statistic and machine learning methods, deep learning-based approaches typically demonstrate superior predictive performance because of their strong non-linear fitting capabilities and flexible model structures. Through a fully connected network with one hidden layer, FNN can learn intricate spatial-temporal dependencies and correlations for future state prediction. In the experiment, FNN outcomes better predictive performance than the whole statistic and machine learning methods in Scenario 1. Nonetheless, the insufficient performance of FNN in Scenario 2 also indicates the need for deep learning-based approaches to improve predictive abilities in a broad spatial area. AGCRN effectively captures temporal and spatial correlations by employing RNN and GCN with adaptive parameters. Therefore, it exhibits excellent predictive performance, especially in Scenario 2. Similarly, GWN also adopts GCN and incorporates an adaptive dependency matrix to capture hidden spatial dependencies and correlations. Through stacked dilated causal convolution layers, GWN overcomes the challenge of long sequence dependencies in AGCRN, enabling it to accurately predict future traffic states in both Scenario 1 and Scenario 2.