A Dynamic Temporal Self-attention Graph Convolutional Network for
Traffic Prediction
- URL: http://arxiv.org/abs/2302.10428v1
- Date: Tue, 21 Feb 2023 03:51:52 GMT
- Title: A Dynamic Temporal Self-attention Graph Convolutional Network for
Traffic Prediction
- Authors: Ruiyuan Jiang, Shangbo Wang, Yuli Zhang
- Abstract summary: This paper proposes a temporal self-attention graph convolutional network (DT-SGN) model which considers the adjacent matrix as a trainable attention score matrix.
Experiments demonstrate the superiority of our method over state-of-art model-driven model and data-driven models on real-world traffic datasets.
- Score: 7.23135508361981
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Accurate traffic prediction in real time plays an important role in
Intelligent Transportation System (ITS) and travel navigation guidance. There
have been many attempts to predict short-term traffic status which consider the
spatial and temporal dependencies of traffic information such as temporal graph
convolutional network (T-GCN) model and convolutional long short-term memory
(Conv-LSTM) model. However, most existing methods use simple adjacent matrix
consisting of 0 and 1 to capture the spatial dependence which can not
meticulously describe the urban road network topological structure and the law
of dynamic change with time. In order to tackle the problem, this paper
proposes a dynamic temporal self-attention graph convolutional network (DT-SGN)
model which considers the adjacent matrix as a trainable attention score matrix
and adapts network parameters to different inputs. Specially, self-attention
graph convolutional network (SGN) is chosen to capture the spatial dependence
and the dynamic gated recurrent unit (Dynamic-GRU) is chosen to capture
temporal dependence and learn dynamic changes of input data. Experiments
demonstrate the superiority of our method over state-of-art model-driven model
and data-driven models on real-world traffic datasets.
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