Spatio-Temporal Graph Convolutional Networks for Traffic Prediction Considering Multiple Spatio-Temporal Information

As an indispensable part of smart city development, traffic prediction's fundamental challenge is effectively modeling complex spatio-temporal dependencies in traffic data. Although previous work has made great efforts to learn the temporal dynamics and spatial dependencies of traffic, the following challenges still exist. Firstly, time series has multi-scale char-acteristics, meaning that traffic series display different trends at various time scales and change over time. Secondly, the spatial relationships within a traffic network are not singular. The traffic condition of a node is influenced not only by nearby nodes but also potentially by distant nodes. To this end, we propose a novel framework, spatio-temporal graph convolutional networks considering multiple spatio-temporal information (STGCN-MI), for traffic prediction. Specifically, in the temporal dimension, we design a multi-scale local multi-head self-attention module. It more appropriately assigns correlation strengths to data pairs in the temporal dimension by significantly enhancing the ability to represent trends in the series at different time scales, thereby capturing dynamic temporal correlations more accurately. In the spatial dimension, we develop a fusion graph convolution module, which mines the multiple spatial dependencies in the traffic network and fuses them adaptively. Extensive experiments on two real-world datasets demonstrate that the effectiveness and superiority of our methods.