###### Abstract  
The rapid expansion of large urban areas underscores the critical importance of road infrastructure. An accurate understanding of traffic flow on road networks is essential for enhancing civil services and reducing fuel consumption. However, traffic flow is influenced by a complex array of factors and perpetually changing conditions, making comprehensive prediction of road network behavior challenging. Recent research has leveraged deep learning techniques to identify and forecast traffic flow and road network conditions, enhancing prediction accuracy by extracting key features from diverse factors. In this study, we performed short-term traffic speed predictions for road networks using data from Moblieye sensors mounted on taxis in Daegu City, Republic of Korea. These sensors capture the road network flow environment and the driver's intentions. Utilizing these data, we integrated convolutional neural networks (CNNs) with spatio-temporal graph convolutional networks (STGCNs). Our experimental results demonstrated that the combined STGCN and CNN model outperformed the standalone STGCN and CNN models. The findings of this study contribute to the advancement of short-term traffic speed prediction models, thereby improving road network flow management. t +
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However, predicting information about road networks is challenging, owing to various factors and constantly changing complex structures. To more accurately predict the state of the road network affected by various factors, researchers have been working to improve the performance of the model using various data [6]. For example, the identification of temporal cycles of the movement of people helps in traffic flow prediction [7], and applying weather information (e.g., sunshine hours, temperature, and rainfall) provides more accurate traffic predictions [8]. Furthermore, other data, such as road specifications, vehicle types, social network data, and pandemic data, have been used to capture the characteristics of the road network [9; 10; 11]. Despite these advancements, existing models often face limitations in integrating multiple data types effectively and capturing both spatial and temporal dependencies in traffic data. Additionally, the intention of drivers, which can significantly influence traffic flow, needs to be addressed. This research presents more holistic and accurate traffic speed prediction models by integrating diverse data sources and advanced modeling techniques. In this study, we used speed, brake, and traffic sign recognition (TSR) data to predict traffic flow. These data were collected by Mobiley sensors attached to taxis. TSR data represent how much a driver is speeding when the traffic sign is detected by the camera installed in the vehicle. It keeps the driver informed of speed limit changes. We used the TSR data to reflect the intention of the driver according to the environment of a given road network. Furthermore, we used the brake data in conjunction with TSR data to better understand the intention of drivers. Although various data play a crucial role in traffic flow prediction, the appropriate model selection is also substantial to extract appropriate features and perform accurate traffic flow predictions. The methodologies for short-term traffic speed prediction can be broadly classified as statistical methods, machine learning methods, and deep learning methods [12; 13; 14]. Recently, deep learning approaches have been frequently applied to efficiently capture traffic flow characteristics [15; 16; 17; 18]. For example, a recurrent neural network (RNN) model can consider the temporal characteristics of the road traffic network, whereas a convolutional neural network (CNN) model can reflect its spatial characteristics. A hybrid approach was also proposed to complement each other using RNN and CNN [19]. Additionally, a graph neural network (GNN) model was developed to reflect the relational information of the road network [20].