Residual Graph Convolutional Recurrent Networks For Multi-step Traffic
Flow Forecasting
- URL: http://arxiv.org/abs/2205.01480v1
- Date: Tue, 3 May 2022 13:23:38 GMT
- Title: Residual Graph Convolutional Recurrent Networks For Multi-step Traffic
Flow Forecasting
- Authors: Wei Zhao, Shiqi Zhang, Bing Zhou and Bei Wang
- Abstract summary: We propose a new Spatial-temporal forecasting model, namely the Residual Graph Convolutional Recurrent Network (RGCRN)
The model uses our proposed Residual Graph Convolutional Network (ResGCN) to capture the fine-grained spatial correlation of the traffic road network.
Our comparative experimental results on two real datasets show that RGCRN improves on average by 20.66% compared to the best baseline model.
- Score: 12.223433627287605
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Traffic flow forecasting is essential for traffic planning, control and
management. The main challenge of traffic forecasting tasks is accurately
capturing traffic networks' spatial and temporal correlation. Although there
are many traffic forecasting methods, most of them still have limitations in
capturing spatial and temporal correlations. To improve traffic forecasting
accuracy, we propose a new Spatial-temporal forecasting model, namely the
Residual Graph Convolutional Recurrent Network (RGCRN). The model uses our
proposed Residual Graph Convolutional Network (ResGCN) to capture the
fine-grained spatial correlation of the traffic road network and then uses a
Bi-directional Gated Recurrent Unit (BiGRU) to model time series with spatial
information and obtains the temporal correlation by analysing the change in
information transfer between the forward and reverse neurons of the time series
data. Our comparative experimental results on two real datasets show that RGCRN
improves on average by 20.66% compared to the best baseline model. You can get
our source code and data through https://github.com/zhangshqii/RGCRN.
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