Position-Invariant Graph Convolutional Recurrent Network for Traffic Forecasting

Abstract

Traffic forecasting leverages multivariate time series analysis to predict traffic patterns. Real-world traffic data comprises two distinct types of latent time-series signals: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">diffuse signals</i>, which refer to time-varying information propagated across the traffic network, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">intrinsic signals</i>, which capture unique, location-specific patterns. However, existing approaches often treat traffic signals solely as diffusion outcomes, overlooking the intrinsic characteristics that can significantly influence model performance. To address this issue, we propose the Position-invariant Graph Convolutional Recurrent Network (PGCRN), which decouples diffuse and intrinsic signals for improved traffic forecasting. Instead of relying on a predefined graph, PGCRN learns graph structures from spatio-temporal data through a learnable position-invariant node representation that forms an adaptive adjacency matrix. This is integrated into a Graph Convolutional Recurrent Network (GCRN) encoder–decoder to jointly capture spatial and temporal dependencies. Furthermore, we introduce a contrastive learning framework in which a node’s time-varying and position-invariant representations form positive pairs, while position-invariant representations from different nodes form negative pairs. The model is trained with a triplet loss. Experiments on four benchmark datasets show that PGCRN consistently outperforms strong baselines. Owing to its computational efficiency, PGCRN is also well suited for deployment on resource-constrained edge devices.