Attention-Based Spatiotemporal Adaptive Graph Diffusion Convolutional Network For Traffic Flow Prediction

Abstract

Accurate traffic flow prediction (TFP) is the basis for building intelligent transportation systems in smart cities. Existing methods usually focus on capturing the spatial dependencies of static graph structures, ignoring the explicit road network structure and failing to mine the embedded spatiotemporal dependencies and characteristics of traffic network structures. To this end, we propose an attention-based spatiotemporal adaptive graph diffusion convolutional network (AST-AGDCN) to accurately describe the spatial structure and fully characterize the spatiotemporal dependencies of the traffic data for TFP. Specifically, we designed a spatiotemporal self-attention mechanism module for extracting the potential effects of temporal patterns and spatial correlations among traffic network nodes and mining the degree of influence of the changing spatiotemporal location. A road network connectivity graph modeling module was then constructed to adaptively extract spatial relationships between nodes to learn external information. Next, a diffusion convolution network was built to model the directionality and dynamics of the spatial road network structure, and the complex spatial structural properties between the regions were aggregated into a feature matrix to discover hidden spatial pattern correlations effectively. Finally, temporal features of the dynamic traffic flow were explicitly modeled using a bidirectional gated recurrent unit, and weights were fused to the results of the recent, daily, and weekly periodic segments for TFP. The extensive experimental results demonstrated that AST-AGDCN was superior to comparable models in prediction accuracy on real-world datasets.