Spatiotemporal Factorized Graph Neural Networks for Joint Large-Scale Traffic Prediction and Online Pattern Recognition

Exploring the network-level spatiotemporal traffic interplay through traffic pattern recognition or traffic prediction is essential for intelligent traffic systems. Prediction models commonly lack interpretability, while the static and unsupervised insights from pattern recognition underperform in dynamic and stochastic applications. Therefore, the authors developed a spatiotemporal factorized graph neural network (STF-GNN) framework to integrate traffic prediction and pattern recognition processes. Unlike prevailing prediction models, it shifts the prediction variables from traffic states to latent informative patterns. Specifically, graph-based and time-series models are applied to predict the spatial and temporal patterns, respectively, during the prediction horizons. Traffic predictions are derived based on the latent patterns, enhancing the model interpretability. In addition, decoupling the spatiotemporal correlations enables more stable and scalable pattern recognition. Thus, a pretrained pattern-driven model can be transferred to longer prediction horizons. The proposed framework was applied to two highway traffic datasets and two public transit datasets. Short-term prediction experiments reveal significant improvements in prediction and accuracy. Transfer learning experiments were also conducted. The predictors with superior accuracy in short-term scenarios perform effectively over extended horizons, demonstrating the proposed framework’s ability to provide precise predictions without compromising the validity of the recognized patterns. The patterns capture both predominant and marginal traffic characteristics during a relatively long horizon, supporting the prediction accuracy, interpretability, and scalability.