{"type":"ScholarlyArticle","title":"GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction","authors":[{"name":"Shen Fang","slug":"shen-fang","given":"Shen","family":"Fang","affiliation":"NLPR , Institute of Automation , Chinese Academy of Sciences; School of Artificial Intelligence , University of Chinese Academy of Sciences"},{"name":"Qi Zhang","slug":"qi-zhang","given":"Qi","family":"Zhang","affiliation":"NLPR , Institute of Automation , Chinese Academy of Sciences; School of Artificial Intelligence , University of Chinese Academy of Sciences"},{"name":"Gaofeng Meng","slug":"gaofeng-meng","given":"Gaofeng","family":"Meng","affiliation":"NLPR , Institute of Automation , Chinese Academy of Sciences; School of Artificial Intelligence , University of Chinese Academy of Sciences"},{"name":"Shiming Xiang","slug":"shiming-xiang","given":"Shiming","family":"Xiang","affiliation":"NLPR , Institute of Automation , Chinese Academy of Sciences; School of Artificial Intelligence , University of Chinese Academy of Sciences"},{"name":"Chunhong Pan","slug":"chunhong-pan","given":"Chunhong","family":"Pan","affiliation":"NLPR , Institute of Automation , Chinese Academy of Sciences"}],"abstract":"Predicting traffic flow on traffic networks is a very challenging task, due to the complicated and dynamic spatial-temporal dependencies between different nodes on the network. The traffic flow renders two types of temporal dependencies, including short-term neighboring and long-term periodic dependencies. What's more, the spatial correlations over different nodes are both local and non-local. To capture the global dynamic spatial-temporal correlations, we propose a Global Spatial-Temporal Network (GSTNet), which consists of several layers of spatial-temporal blocks. Each block contains a multi-resolution temporal module and a global correlated spatial module in sequence, which can simultaneously extract the dynamic temporal dependencies and the global spatial correlations. Extensive experiments on the real world datasets verify the effectiveness and superiority of the proposed method on both the public transportation network and the road network.","doi":"10.24963/ijcai.2019/317","url":"https://ijcai.org/proceedings/2019/317","pdf":"https://ijcai.org/proceedings/2019/0317.pdf","markdown":"https://ijcai.org/proceedings/2019/317.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/317","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning A-L","track":"Main track","number":317,"pages":{"first":2286,"last":2293},"keywords":["Machine Learning Applications: Networks","Machine Learning: Deep Learning","Machine Learning: Data Mining","Machine Learning: Time-series;Data Streams","Multidisciplinary Topics and Applications: Transportation"],"videos":[],"references":[{"position":1,"raw":"[Atwood and Towsley, 2016] James Atwood and Don Towsley. 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