Journals
  Publication Years
  Keywords
Search within results Open Search
Please wait a minute...
For Selected: Toggle Thumbnails
Spatio-temporal hyper-relationship graph convolutional network for traffic flow forecasting
Yongkai ZHANG, Zhihao WU, Youfang LIN, Yiji ZHAO
Journal of Computer Applications    2021, 41 (12): 3578-3584.   DOI: 10.11772/j.issn.1001-9081.2021060956
Abstract655)   HTML18)    PDF (1112KB)(248)       Save

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.

Table and Figures | Reference | Related Articles | Metrics