A Multiview Spatial-Temporal Adaptive Transformer-GRU Framework for Traffic Flow Prediction

Abstract

Accurate traffic flow prediction is a key aspect of building data-driven intelligent transportation systems (ITSs) which relies on the Internet of Things (IoT) sensors deployed along roads, and dynamic spatial-temporal dependencies mining is a major area of interest in traffic flow prediction. Existing methods, however, overlook the diversities of traffic flow patterns from the perspectives of temporal and spatial dimensions. To this end, this article presents a multiview spatial-temporal adaptive transformer-GRU (MST-ATG) framework based on the encoder-decoder architecture to capture complex spatial-temporal dependencies from various perspectives. Specifically, a multiview embedding layer (MEL) containing original traffic data and spatial-temporal correlated features is designed to enrich the feature encoding. Then, based on the inherent characteristics of traffic flow, we introduce a periodicity-trend decomposition (PTD) method to fully consider the periodic- and trend-oriented features of time series. Finally, we propose a spatial-temporal adaptive transformer-GRU (ST-ATG) to dynamically extract spatial-temporal dependencies and adaptively choose computation steps in which a temporal adaptive stacked-GRU module (T-AGM) is proposed to extract correlations in temporal dimension and spatial dependencies captured by a spatial adaptive transformer module (S-ATM). Experimental results on six large-scale real-world datasets demonstrate that our MST-ATG framework outperforms the benchmarks in prediction accuracy. For instance, the average root-mean-square error of MST-ATG on PeMS08 is reduced by 48.3%, 41.09%, 12.95%, 17.67%, 18.64%, 2.4%, 14.67%, 9.15%, 1.1%, 2.4%, 2.51%, and 1.2% compared to that of autoregressive integrated moving average, long short-term memory (LSTM), DCRNN, STGCN, ASTGCN, GWNet, STSGCN, AGCRN, Bi-STAT, STAEformer, PDFormer, and STPGNN, respectively.