Coordinate Attention Enhanced Adaptive Spatiotemporal Convolutional Networks for Traffic Flow Forecasting

The prediction of traffic flow has emerged as a pivotal element within the domain of intelligent transport systems, garnering considerable interest and attention from various quarters. SpatioTemporal Graph Neural Networks (STGNNS) have been extensively employed to develop traffic representations. However, the extant research is constrained by several limitations: 1) The majority of STGNNs fail to account for the spatial heterogeneity of traffic data, with the distribution of traffic flows in different regions potentially being biased. This makes it challenging to capture comprehensive traffic flow data features. 2) In the case of complex spatial relationships, the loss of spatial correlation makes it challenging to accurately capture the dynamically variable spatial dependence of traffic flow data. In order to address these challenges, this paper proposes a new coordinate attention enhanced adaptive spatiotemporal convolutional network prediction model (CAAS), which introduces coordinate attention with the objective of modelling spatial heterogeneity and capturing complex spatiotemporal correlations. Moreover, a novel spatial multi-head attention mechanism is introduced with the objective of capturing the intricate interdependencies of multi-scale dynamic spatiotemporal data. This is accomplished by augmenting the input matrix of the softmax function in the attention mechanism, which enhances the differentiation of attention weights and facilitates the capture of spatial relationships at a finer granularity. Ultimately, the prediction of traffic flow is accomplished through the adaptive fusion of temporal and spatial features via a gated fusion mechanism.Experiments conducted on the publicly available PEMS04 and PEMS08 datasets show that the proposed CAAS model significantly surpasses existing cutting-edge methods.