## 2 Related Works
Moreover, gated recurrent units (GRUs) have been developed to make these LSTMs more efficient. GRU was applied to predict the traffic information, and the improvement in accuracy compared to the existing statistical-based model and conventional RNN was confirmed [36]. However, a road network is also affected by its surrounding space and not just the previous state. RNN-based deep learning approaches, such as LSTM and GRU, can predict traffic flow by considering temporal features but not spatial features. CNN, primarily used in computer vision, can be used to address this problem by constructing images using the links and time that make up the road network. The method of reflecting temporal and spatial characteristics by converting traffic data in the road network unit into images enables predictions that reflect the effects of the links to each other in the road network. Additionally, it enables the prediction of traffic information in a road network on a large scale. The study on predicting speed information in road networks by applying CNNconfirmed that the learning time was significantly shorter than the existing statistical-based model and more accurate than the ANN, RNN, and LSTM [37; 38]. CNN-based algorithms can capture and utilize the information that previous methods cannot reflect with limited data. However, the structure of the CNN model does not have long-term dependency. A complementary hybrid model was proposed to alleviate these limitations by combining each algorithm. Considering the characteristics of CNN- and RNN-based models, a model was developed to consider spatial and temporal features simultaneously [19]. Another study used features extracted by CNN as input to an RNN-based model, decoding them again [39]. A method of extracting and converging various data using CNN and GRU algorithms was explored [40] alongside a study applying traffic engineering theory [41]. The hybrid model mentioned earlier applies new data to learning and fuses several algorithms, showing high performance. However, these processes need additional work for relationship information. Targets placed in similar environments may undergo similar patterns of change. Moreover, similar environments can be spatially closed. Therefore, applying a method that can consider these graph-based relationships can be crucial in improving accuracy. A study [42] encoded non-Euclidean correlations into multiple graphs and then applied a spatio-temporal multi-graph convolutional network. The graph-based approach has been used in other studies to increase model performance [43; 44]. To implement a model with better performance, converging and applying various data together, including non-Euclidean information, are necessary. Efforts to deal with this graph-based information have been made not only by using the deep learning method but also by using the XGBoost-based method among the ensemble models [45]. Additionally, applying multiple data to each algorithm has stimulated further research as newly collectible data increase [46; 47; 48]. For example, the pedal information was analyzed using sensors installed inside the vehicle to reflect the intention of the driver, thus confirming better performance [49]. Therefore, this study presents a hybrid model that can perform short-term traffic speed prediction at the scale of the road network by reflecting the road network environment and intention of the driver, along with the relational information of the road network. STGCN was used to reflect the road network-related information and consider long-term dependencies. Then, this study combined STGCN with CNN to improve traffic speed prediction. Furthermore, traffic speed, TSR, and brake data were used to reflect environmental information and the intention of the driver in the road network. TSR data are an index that indicate the excess speed based on the speed limit presented in the traffic sign in the road network environment. These data reflect the intention of the driver in a given road network environment.