CLEAR: Spatial-Temporal Traffic Data Representation Learning for Traffic Prediction

Abstract

In the evolving field of urban development, precise traffic prediction is essential for optimizing traffic and mitigating congestion. While traditional graph learning-based models effectively exploit complex spatial-temporal correlations, their reliance on trivially generated graph structures or deeply intertwined adjacency learning without supervised loss significantly impedes their efficiency. This paper presents Contrastive Learning of spatial-tEmporal trAffic data Representations (CLEAR) framework, a comprehensive approach to spatial-temporal traffic data representation learning aimed at enhancing the accuracy of traffic predictions. Employing self-supervised contrastive learning, CLEAR strategically extracts discriminative embeddings from both traffic time-series and graph-structured data. The framework applies weak and strong data augmentations to facilitate subsequent exploitations of intrinsic spatial-temporal correlations that are critical for accurate prediction. Additionally, CLEAR incorporates advanced representation learning models that transmute these dynamics into compact, semantic-rich embeddings, thereby elevating downstream models’ prediction accuracy. By integrating with existing traffic predictors, CLEAR boosts predicting performance and accelerates the training process by effectively decoupling adjacency learning from correlation learning. Comprehensive experiments validate that CLEAR can robustly enhance the capabilities of existing graph learning-based traffic predictors and provide superior traffic predictions with a straightforward representation decoder. This investigation highlights the potential of contrastive representation learning in developing robust traffic data representations for traffic prediction.