Spatio-Temporal Residual Graph Attention Network for Traffic Flow Forecasting

Accurate spatio-temporal traffic flow forecasting is significant for the modern traffic management and control. In order to capture the spatio-temporal characteristics of the traffic flow simultaneously, we propose a novel spatio-temporal residual graph attention network (STRGAT). First, the network...

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Bibliographic Details
Published inIEEE internet of things journal p. 1
Main Authors Zhang, Qingyong, Li, Changwu, Su, Fuwen, Li, Yuanzheng
Format Journal Article
LanguageEnglish
Published IEEE 07.02.2023
Subjects
attention mechanism
Convolution
Convolutional neural networks
Correlation
Feature extraction
Forecasting
graph attention network
Predictive models
Roads
spatio-temporal characteristics
Traffic flow forecasting
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Summary:Accurate spatio-temporal traffic flow forecasting is significant for the modern traffic management and control. In order to capture the spatio-temporal characteristics of the traffic flow simultaneously, we propose a novel spatio-temporal residual graph attention network (STRGAT). First, the network adopts a deep full residual graph attention block, which performs a dynamic aggregation of spatial features regarding the node information of the traffic network. Second, a sequence-to-sequence block is designed to capture the temporal dependence in the traffic flow. The traffic flow data with weekly periodic dependencies are also integrated and STRGAT is used for traffic forecasting of traffic road networks. The experiments are conducted on three real datasets in California, USA. Results verify that our proposed STRGAT is able to learn the spatio-temporal correlation of traffic flow well and outperforms the state-of-the-art methods.
ISSN:2327-4662
DOI:10.1109/JIOT.2023.3243122