关键词: 交通流预测, 时空图神经网络, 时空依赖性, 模型设计, 模型分类标准

Abstract: In recent years, the application of deep learning in traffic flow prediction has attracted wide attention, especially the spatio-temporal graph neural network has achieved remarkable success in capturing spatio-temporal dependencies and predicting traffic characteristics. Although some reviews have examined the application of spatio-temporal graph neural networks, most of these studies focus primarily on application scenarios and fail to provide an in-depth analysis from the perspective of model design. Furthermore, a unified model classification framework is absent. This paper proposes a hierarchical classification method that considers the key elements such as module selection, fusion mechanism, architecture design, and training strategy. The spatio-temporal graph neural networks can be divided into six categories, namely recurrent graph convolutional network, spatio-temporal fully convolutional network, spatio-temporal attention network, spatio-temporal encoder network, spatio-temporal hybrid architecture network, and spatio-temporal networks with additional strategies. For each category, the unique model construction and fusion mechanisms are analyzed in detail, and the different model variants are compared. By analyzing both representative and recent works, the development trend of spatio-temporal graph neural networks is discussed, and the code addresses of open-source models are provided. Subsequently, the commonly used public datasets are gathered, and the performance of the latest advanced models is visually analyzed by comparing the results of previous experiments. Finally, the development opportunities and challenges in this field are summarized to offer insights for future research.

Key words: traffic flow prediction, spatio-temporal graph neural networks, spatio-temporal dependencies, model design, model classification criteria