Traffic prediction based on multi-scale adaptive interpretable dynamic spatiotemporal graph convolutional network

Abstract Amidst rapid urbanization, traffic congestion and frequent accidents pose significant challenges to urban development. In this scenario, precise traffic prediction is imperative for enhancing the efficiency and safety of traffic management systems. Traditional approaches often fail to capture the complex spatiotemporal correlations and multi-level temporal dynamics of traffic data, rendering them inadequate for long-term traffic prediction. To address these limitations, this study introduces a novel approach employing an interpretable multi-scale adaptive dynamic spatiotemporal graph convolutional network. This method dynamically extracts latent relationships through a graph learning module to enhance traffic prediction accuracy and augments interpretability through a thorough analysis of data flow dynamics. Moreover, the model incorporates a dynamic graph construction technique within the prediction module to better model complex traffic scenarios, and introduces a scale fusion method to adaptively select different hierarchical levels of analysis. Comprehensive experiments on four real-world datasets-England, METR-LA, PEMSD4, and PEMSD8-demonstrate that our proposed model surpasses the best baselines by 32.6%, 7.37%, 25.9%, and 0.7% in mean absolute error, respectively, achieving an average improvement of 16.65%. Additionally, by analyzing the graph convolutional network node weight parameters and monitoring weight variations across different layers, we elucidate the model’s decision-making process, thereby providing an interpretative analysis of its functionality.