## 5 Discussion and Conclusions  
In this study, we presented a hybrid model for a deeper understanding of road infrastructure, which is growing in size and importance with the development of urban areas. The hybrid model predicts the traffic speed for road networks that are affected by various variables and have a complex structure in which states constantly change. The model presented in this study is a fusion of STGCN and CNN, which reflects spatio-temporal features and considers graph-based relationship features to make predictions. Additionally, the features for the models were extracted from TSR and brake data to reflect the intention of the driver. The experimental results demonstrated that the proposed model outperformed the STGCN and CNN models for traffic speed prediction. There are statistically significant dif \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Predictions after**} & \multicolumn{2}{c}{**STGCN (Speed) + CNN (TSR,**} & \multirow{2}{*}{**STGCN (Speed) + CNN (Brake)**} \\ \(\mathbf{n-1}\) **Time Step** & & & \\ \cline{2-4}  & **MAE** & **RMSE** & **MAE** & **RMSE** \\ \hline
60 & 17.0332 & 20.7712 & 17.0403 & 20.8899 \\
70 & 17.0503 & 20.7862 & 17.2428 & 20.9741 \\
80 & 17.1464 & 20.9856 & 17.0897 & 20.9794 \\
90 & 17.0812 & 20.8542 & 17.0721 & 20.8225 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prediction results based on the presence or absence of TSR data. ferences between the proposed model and the alternative models using MAE. Furthermore, there was a large effect size between the proposed model and others. Therefore, it seems reasonable to conclude that using the proposed model is more effective statistically and practically than using alternative models, such as STGCN and CNN. In previous studies, STGCN- and CNN-based models showed better performance by reflecting multiple factors compared to RNN-based models, which actually have difficulty reflecting time series with multiple factors. CNN-based models are suitable for parallel processing, can perform tasks quickly, and cannot be significantly affected by sequence length, unlike RNN-based models. STGCN is also specialized in processing data in a graph and is suitable for analyzing transportation networks that represent relationships between nodes. Like CNN, it is suitable for parallel processing and allows complex models to be handled more efficiently than RNN, which proceeds sequentially. Furthermore, unlike CNN, STGCN can have long-term dependency through GLU. This can positively affect prediction performance if it is time-dependent and shows some degree of correlation. However, the prediction performance can be decreased when the temporal correlation is very low. Therefore, it is essential to consider whether the data fit with the selected model. In that context, the hybrid model proposed in this study was appropriate and generated optimal predictions according to the characteristics and data of each model. Moreover, we investigated the effect of data, such as TSR. We applied TSR to the proposed model and removed it from the proposed model. While there were no statistically significant differences between the models, there was a medium effect size. Therefore, the model with TSR practically performed better than the model without TSR. In summary, we proposed a hybrid model based on STGCN and CNN. The structure of the proposed model uses graph data and image data together. We used STGCN to train speed data and CNN to train TSR and brake information. Additionally, this study utilized TSR data collected from Mobileye sensors to reflect the intention of the driver for traffic speed prediction. The results of this study are expected to provide a deep understanding and insight into the traffic flow on road networks, which plays a vital role in developing cities. This is expected to contribute to the efficient operation and improvement of transportation networks, reduce unnecessary fuel combustion, and have a positive impact on environmental, economic, and time costs.