A macro-micro spatio-temporal neural network for traffic prediction

Accurate traffic prediction is crucial for planning, management and control of intelligent transportation systems. Most state-of-the-art methods for traffic prediction effectively capture complex traffic patterns (e.g. spatial and temporal correlations of traffic data) by employing spatio-temporal neural networks as prediction models, together with graph convolution networks to learn spatial correlations of prediction objects (e.g. traffic states of road segments, as in this study). Such spatial correlations can be regarded as micro correlations. However, there are also macro correlations between regions, each of which is composed of multiple road segments or artificially partitioned areas. Macro correlations represent another type of interaction within road segments, and should be carefully considered when predicting traffic. The diversity of micro spatial correlations and corresponding macro spatial correlations (e.g. correlations based on physical proximity or traffic pattern similarity) further increases the complexity of traffic prediction. We overcome these challenges by developing a macro-micro spatio-temporal neural network model, denoted 'MMSTNet'. MMSTNet captures spatio-temporal patterns by (a) utilizing a graph convolution network and a spatial attention network to capture micro and macro spatial correlations, respectively; (b) employing a temporal convolution network and a temporal attention network to learn temporal patterns; and (c) integrating hierarchically learned representations based on designed attention mechanisms. We perform evaluations on two real world datasets and thereby demonstrate that MMSTNet outperforms state-of-the-art models in traffic prediction tasks.