Robust Traffic Forecasting With Disentangled Spatiotemporal Graph Neural Networks

Traffic prediction is a cornerstone of intelligent transportation systems (ITSs). The effectiveness of existing spatiotemporal graph neural networks (STGNNs) heavily relies on the independent identically distributed (i.i.d.) assumption of traffic data, which is frequently violated in practice because of distribution shifts owing to exogenous factors. While learning features that remain stable across all environments is promising for modeling robust frameworks, the fundamental challenge involves the decomposition of invariant features from the dynamic nature of spatiotemporal dependencies. In this article, we propose the disentangled spatiotemporal (DIST) graph neural networks, a novel framework for robust traffic forecasting considering distribution shifts. In DIST, latent invariant variables are explicitly decoupled from dynamically evolving spatiotemporal dependencies, enabling the learning of topology-agnostic representations resilient to distribution shifts. Specifically, we formulate a causality-driven learning objective that guides the separation of invariant variables from various exogenous factors. We then propose a spatiotemporal graph modeling module that can adaptively capture spatiotemporal dependencies in evolving traffic systems. Furthermore, we present a graph perturbation module to simulate topology variations during training, thereby encouraging the model to identify perturbation-sensitive dependencies and infer invariant and variant features for prediction and intervention tasks. The prediction risk and its variance on multiple interventional distributions are minimized in our learning strategy, allowing the model to identify invariant features, thus improving its robustness. The results of comprehensive real-world experiments demonstrate the superiority of our approach. The source code is available: https://github.com/tingwang25/DIST.