GraphSAGE-Based Dynamic Spatial-Temporal Graph Convolutional Network for Traffic Prediction

• Published in: IEEE transactions on intelligent transportation systems Vol. 24; no. 10; pp. 1 - 15
• Main Authors: Liu, Tao, Jiang, Aimin, Zhou, Jia, Li, Min, Kwan, Hon Keung
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Computational modeling; Convolution; Correlation; Data models; Deep learning; Feature extraction; GraphSAGE; Predictive models; Roads; spatial correlations; spatial–temporal embedding; Traffic information; traffic prediction; urban road networks; weights learning
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2023.3279929

Traffic networks exhibit complex spatial-temporal dependencies, and accurately capturing such dependencies is critical to improving prediction accuracy. Recently, many deep learning models have been proposed for spatial-temporal dependency modeling. While numerous deep learning models have been developed for spatial-temporal dependency modeling, most rely on different types of convolutions to extract spatial and temporal correlations separately. To address this limitation, we propose a novel deep learning framework for traffic prediction called GraphSAGE-based Dynamic Spatial-Temporal Graph Convolutional Network (DST-GraphSAGE), which can capture dynamic spatial and temporal dependencies simultaneously. Our model utilizes a spatial-temporal GraphSAGE module to extract localized spatial-temporal correlations from past observations of a node's spatial neighbors. Meanwhile, the attention mechanism is incorporated to dynamically learn weights between traffic nodes based on graph features. Additionally, to capture long-term trends in traffic data, we employ dilated causal convolution as the temporal convolution layer. A series of numerical experiments are conducted on five real-world datasets, which demonstrates the effectiveness of our model for spatial-temporal dependency modeling.