关键词: 交通工程；交通预测;智能交通系统;时空多头注意力;图神经网络; 自适应图结构

Abstract: Accurate traffic prediction is pivotal for intelligent transportation systems (ITS). However, due to complex time and space dependencies, the existing traffic flow prediction methods have failed to effectively capture the spatiotemporal characteristics of the road network and also ignored the strong dynamic nature of the correlation of road network traffic data in both spatial and temporal dimensions. In order to furtherly improve the prediction accuracy, a spatiotemporal graph neural network model based on multi-head attention was proposed. Firstly, the proposed model constructed an adaptive graph structure learning component that could effectively capture the dynamic spatiotemporal correlation of the graph structure. Secondly, the proposed model respectively designed a temporal multi-head attention module and a spatial multi-head attention module, which were based on the attention mechanism. The designed spatiotemporal multi-head attention module could effectively extract the spatiotemporal features of the road network. Finally, the stacked spatiotemporal convolutional layers were used to predict the future traffic conditions. The results, derived from open-source datasets, demonstrate that the proposed model performs well in spatiotemporal feature extraction and long-term prediction, and achieves more accurate prediction results than baseline methods.

Key words: traffic engineering; traffic prediction; intelligent transportation system; spatiotemporal multi-head attention; graph neural network; adaptive graph structure