ST-NAMN: a spatial-temporal nonlinear auto-regressive multichannel neural network for traffic prediction

Abstract

Accurate and efficient traffic information prediction is significantly important for the management of intelligent transportation systems. The traffic status (e.g., speed or flow) on one road segment is spatially affected by both its nearby neighbors and distant locations. The impending traffic status can be temporally influenced not only by its recent status but also by the randomness of its historical status change. The current state-of-the-art methods have effectively captured the spatio-temporal dependencies of road networks. However, most existing methods overlook the impact of time delay when capturing dynamic time dependencies. In addition, aggregating roads with similar traffic patterns from a wide range of spatial associations still poses challenges. In this paper, a spatial-temporal nonlinear auto-regressive multi-channel neural network (ST-NAMN) model is proposed to reveal the sophisticated nonlinear dynamic interconnections between temporal and spatial dependencies in road traffic data. Considering the temporal periodicity and spatial pattern similarity inherently in road traffic data, a divided period latent similarity correlation matrix (DLSC) first is utilized to calculate the similarity of traffic patterns from historical observation data. Then, we introduce an output feedback to the multi-layer perceptron (MLP) through a delay unit, which enables the output-layer to feedback its result data to the input layer in real-time, and further participate in the next iterative training to boost the learning capacity. Furthermore, an Enhanced-Bayesian Regularization weight updating method (EBR) is designed to better contemplate the influence of the continuous and delayed observation points compared to existing optimizers during the learning procedure. Experimental tests have been carried out on four real-world datasets and the results demonstrated that the proposed ST-NAMN method outperforms other state-of-the-art models.