Study on Intersection Delay Prediction Based on Spatiotemporal Graph Convolutional Network Using Delay Big Data

ABSTRACT Road intersections are the key nodes in urban road networks, and their operational efficiency affects the dynamic balance of regional traffic flow. However, classical intersection delay models are unable to quickly and efficiently predict large‐scale intersection delays. This paper proposes a spatiotemporal graph convolutional network (STGCN) intersection delay prediction model based on delay big data. By introducing a temporal attention mechanism, the model can capture key temporal information and enhance the ability to model the spatio‐temporal evolution law of intersection delays. First, based on traffic platform delay big data, external factor data, and road network structure, the historical delay matrix, the external factor matrix, and intersection adjacency matrix are constructed, respectively. Next, the graph convolutional network (GCN) is used to extract spatial features of intersection delay. A temporal attention mechanism is then introduced to assign weights to different time steps, thereby enhancing the model's perception of critical time steps. Finally, the temporal convolutional network (TCN) is employed to extract temporal features of intersection delay, which are used to predict future intersection delays. Experimental results show that, compared with the optimal benchmark model STGCN, the proposed model reduces the MAE, RMSE, and MAPE metrics by 3.80%, 2.75%, and 4.23%, respectively. This study on intersection delay prediction using a STGCN based on delay big data not only improves the efficiency of intersection delay prediction but also provides a theoretical basis for traffic management departments to formulate measures.This study focuses on the delay prediction problem of intersections, key nodes in urban road networks. Aiming at the deficiency that classical models are difficult to efficiently predict the delays of large‐scale intersections, a STGCN model based on delay big data is proposed. This model constructs the historical delay matrix, the external factor matrix and the intersection adjacency matrix, uses the GCN to extract spatial features, combines the temporal attention mechanism to enhance the perception of key periods, and then uses the TCN to mine temporal features to achieve delay prediction. Experiments show that, compared with the optimal benchmark model STGCN, its MAE, RMSE, and MAPE indicators are reduced by 3.80%, 2.75%, and 4.23%, respectively, providing theoretical support for improving the efficiency of intersection delay prediction and traffic management decision‐making.