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GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction
lib:491504b688a9887a (v1.0.0)
Authors: Shen Fang 1,2,Qi Zhang 1,2,Gaofeng Meng 1,2,Shiming Xiang 1,2 and Chunhong Pan 1
Where published: IJCAI-19 2019 1
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: https://www.ijcai.org/proceedings/2019/0317.pdf
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. Tocapturetheglobaldynamicspatial-temporalcorrelations, 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.
Where published: IJCAI-19 2019 1
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: https://www.ijcai.org/proceedings/2019/0317.pdf
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. Tocapturetheglobaldynamicspatial-temporalcorrelations, 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.
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