## 4 Experimental Results  
In this study, we integrated STGCN and CNN models to predict the traffic speed of the road network in network units. Predictions were made by reflecting the environment constituting the road network and the intention of the drivers. Thus, this study used speed, TSR, and brake information. Furthermore, we compared the performance of the proposed model with that of the existing methods. The baseline models were STGCN and CNN. We compared the accuracy by considering a combination of all the variables and methods. In terms of the experimental environments, all methods were coded in Python. Experiments were conducted on a computer with an NVIDIA GeForce RTX 3080 GPU and 10 GB of memory. Table 2 indicates the parameters used in this study. Figure 7: Short-term traffic speed prediction system architecture. To improve the performance of deep learning models and to find the optimal model configuration, various hyperparameters must be adjusted. Hyperparameters affect the model training process. Thus, it is vital to set them correctly. The hyperparameter tuning process involves experimentally adjusting various settings, such as the learning rate of the model, mini-batch size, dropout rate, etc., to find the best combination. Currently, Grid Search is widely used because it helps systematically search for combinations of multiple hyperparameters. Grid Search generates all possible combinations of hyperparameters of interest in a predefined grid, learns a model for each combination, and evaluates performance. Through these iterative experiments, we find the best hyperparameter combination. This process is an essential step to optimize model performance and prevent overfitting. Grid Search allows one to perform the hyperparameter tuning process systematically and helps to find the best model settings; therefore, we used Grid Search to tune our hyperparameters. The epoch was set to 200. If the validation loss did not improve at all during the training process, the training process was stopped even though the epoch is more than 100 times. The batch size was 256, learning rate was 0.001, dropout probability was 0.5, and the optimizer was Adam. In the experiments, we conducted multi-step speed prediction of the road network at several time steps, including 60 min, 70 min, 80 min, and 90 min. STGCN, CNN, and the proposed model were all trained using speed, brake, and TSR data. The experimental results confirmed that the proposed model showed the best performance in all time step intervals. Specifically, the proposed hybrid model, which integrates STGCN and CNN, outperformed the individual STGCN and CNN models. This suggests that the hybrid model can capture both spatial and temporal dependencies more effectively than the standalone models. Table 3 shows that the hybrid model consistently achieved lower MAE and RMSE values across all time steps compared to the individual STGCN and CNN models. This improvement is attributed to the hybrid model's ability to leverage the strengths of both models: the spatial understanding of CNN and the spatio-temporal processing capability of STGCN. Figure 8 represents the prediction error maps of road links between the predicted speed and the observed speed using MAE. The colors on the map indicate the magnitude of the prediction error at each road link, with different colors representing different ranges of MAE values. The error distribution shown in Figure 8 indicates that the hybrid model achieves low prediction errors across most road links, demonstrating its effectiveness in capturing the spatio-temporal patterns of traffic speed. The areas with higher prediction errors (yellow \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline CNN layers & 3 \\ STGCN layers & 2 \\ Convolution kernel size & 3 \\ Time step & 60–90 \\ Number of hidden units & 625 \\ Graph convolution kernel size & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters used in this case study.