Generative Adversarial Pre-Training Enabled Spatial–Temporal Graph Modeling of Traffic Data

Spatiotemporal graph modeling aims to model temporally varying and spatially correlated information sequences in the traffic network, serving as a significant approach to capture the complex, dynamic, and nonlinear changes of traffic systems. Consequently, it plays a crucial role in enhancing the reliability of intelligent transportation systems. Existing methods extract traffic data’s features by designing sophisticated spatiotemporal graph neural network models, and have reached a bottleneck. Furthermore, these spatiotemporal graph models adjust their parameters globally across different datasets, limiting their generalizability and universality. Meanwhile, the pre-training and fine-tuning paradigm has greatly progressed in natural language processing. This paper proposes a Generative Adversarial Pre-trained Spatial-Temporal Graph Convolutional Network (GAPSTGCN) to break through the bottleneck of spatiotemporal modeling of traffic data. Specifically, by developing a spatiotemporal graph encoder that incorporates domain knowledge about traffic system behavior and network science. The module can extract the dynamic change trend of data from the fusion process of spatiotemporal features, while introducing a time-gating mechanism that enhances its effectiveness in medium and long-term predictions. Moreover, we embed the encoding network into the generator and discriminator. Subsequently, we employ the concept of adversarial data generation to pre-train the spatiotemporal graph neural networks, enhancing the model’s knowledge emergence capabilities. Besides, we design an adapter for downstream task prediction to bridge the gap between pre-training and prediction tasks, aligning pre-trained features with downstream task features. Extensive experiments indicate that our model outperforms the state-of-the-art on the datasets. Experiments conducted on various types of traffic datasets further demonstrate the model’s generality.