面向交通流量预测的时空超关系图卷积网络

交通流量预测是智能交通系统中的重要研究课题，然而，交通对象（如站点、传感器）之间存在的复杂局部时空关系使得这项研究颇具挑战。尽管以往的一些研究将流量预测问题转化为一个时空图预测问题从而取得了较大的进展，但是它们忽略了交通对象们跨时空维度的直接关联性。目前仍缺乏一种全面建模局部时空关系的方法。针对这一问题，首先提出一种新颖的时空超图建模方案，通过构造一种时空超关系来全面地建模复杂的局部时空关系；然后提出一种时空超关系图卷积网络（STHGCN）预测模型来捕获这些关系用于交通流量预测。在四个公开交通数据集上进行了大量对比实验，结果表明，相比ASTGCN、时空同步图卷积网络（STSGCN）等时空预测模型，STHGCN在均方根误差（RMSE）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）这三个评价指标上均取得了更优的结果，不同模型运行时间的对比结果也表明，STHGCN有着更高的推理速度。

Spatio-temporal hyper-relationship graph convolutional network for traffic flow forecasting

Traffic flow forecasting is an important research topic for the intelligent transportation system， however， this research is very challenging because of the complex local spatio-temporal relationships among traffic objects such as stations and sensors. Although some previous studies have made great progress by transforming the traffic flow forecasting problem into a spatio-temporal graph forecasting problem， in which the direct correlations across spatio-temporal dimensions among traffic objects are ignored. At present， there is still lack of a comprehensive modeling approach for the local spatio-temporal relationships. A novel spatio-temporal hypergraph modeling scheme was first proposed to address this problem by constructing a kind of spatio-temporal hyper-relationships to comprehensively model the complex local spatio-temporal relationships. Then， a Spatio-Temporal Hyper-Relationship Graph Convolutional Network （STHGCN） forecasting model was proposed to capture these relationships for traffic flow forecasting. Extensive comparative experiments were conducted on four public traffic datasets. Experimental results show that compared with the spatio-temporal forecasting models such as Attention based Spatial-Temporal Graph Convolutional Network （ASTGCN） and Spatial-Temporal Synchronous Graph Convolutional Network （STSGCN）， STHGCN achieves better results in Root Mean Square Error （RMSE）， Mean Absolute Error （MAE） and Mean Absolute Percentage Error （MAPE）； and the comparison of the running time of different models also shows that STHGCN has higher inference speed.