DAE-FAN: Simple and Efficient Mining of Periodic Patterns for Traffic Prediction

As a fundamental task of intelligent transportation systems, traffic prediction aims to forecast traffic time series in road networks based on historical observation data to support upper-level applications. Although deep learning models have performed well in this field in recent years, their architectures have become increasingly complex and less efficient and lack sufficient modelling of the intrinsic features of traffic data over time. In this paper, we focus on the special periodicity of traffic data and propose a novel model called DAE-FAN, which designs a dual adaptive embedding mechanism consisting of feature, periodicity, and spatial embedding. Employing self-supervised learning, the combination of these embedding matrices can autonomously represent the periodic changes in traffic features and spatial patterns. In addition, we introduce the Fourier principle to construct the Fourier neural network to enhance the capability of modelling periodicity. Extensive experiments on four large public traffic datasets demonstrate the superior performance and efficiency of DAE-FAN with its simpler structure compared to current traffic prediction models, providing a promising direction for efficiently solving traffic prediction challenges.