Multistep Coupled Graph Convolution With Temporal-Attention for Traffic Flow Prediction
Forecasting traffic flow is significant for intelligent transportation systems (ITS), such as urban road planning, traffic control, traffic planning, and many more. A flow prediction model aims at forecasting the traffic flow of future time slices at certain regions by learning the historical traffi...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9766337/ |
| _version_ | 1818202220020105216 |
|---|---|
| author | Xiaohui Huang Yuming Ye Xiaofei Yang Liyan Xiong |
| author_facet | Xiaohui Huang Yuming Ye Xiaofei Yang Liyan Xiong |
| author_sort | Xiaohui Huang |
| collection | DOAJ |
| description | Forecasting traffic flow is significant for intelligent transportation systems (ITS), such as urban road planning, traffic control, traffic planning, and many more. A flow prediction model aims at forecasting the traffic flow of future time slices at certain regions by learning the historical traffic flow data and environmental information. However, due to the complicated traffic network topology and the dynamicity of traffic patterns in the real world, it is difficult to capture the multi-level spatial dependencies (e.g. global and local impacts to the traffic) and temporal dependencies (e.g. long-term and short-term impacts to the traffic). In this paper, we propose a Multi-step Coupled Graph Convolution Neural network (MCGCN) with temporal attention to capture the spatial and temporal dependencies of different levels in a traffic network, simultaneously, to predict traffic flow. First, a Multi-step Coupled Graph Convolution module (MCGC) is designed to learn the representation of a traffic network by coupling learning the relationship matrices, to capture the different levels’ information of a traffic network. Then, the traffic network information extracted by MCGC is fed into a Multi-step Coupled Graph Gated Recurrent Unit (MCGRU) module to realize the fusion of traffic network information and temporal features. Finally, a Multi-step Coupled Graph Attention mechanism (MCGCAtt) is used to extract the temporal information of historical time steps to predict the future traffic flow. The experiments are conducted on the NYCTaxi and NYCBike datasets, and the evaluation results demonstrate that our proposed model performs better than the nine compared methods. |
| first_indexed | 2024-12-12T03:05:59Z |
| format | Article |
| id | doaj.art-4cafa945c19748d7887ccd4fbecbf5a5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-12T03:05:59Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4cafa945c19748d7887ccd4fbecbf5a52022-12-22T00:40:30ZengIEEEIEEE Access2169-35362022-01-0110481794819210.1109/ACCESS.2022.31723419766337Multistep Coupled Graph Convolution With Temporal-Attention for Traffic Flow PredictionXiaohui Huang0https://orcid.org/0000-0001-7269-4484Yuming Ye1https://orcid.org/0000-0003-4944-6399Xiaofei Yang2Liyan Xiong3School of Information Engineering, East China Jiaotong University, Nanchang, ChinaSchool of Information Engineering, East China Jiaotong University, Nanchang, ChinaFaculty of Science and Technology, University of Macau, Taipa, MacauSchool of Information Engineering, East China Jiaotong University, Nanchang, ChinaForecasting traffic flow is significant for intelligent transportation systems (ITS), such as urban road planning, traffic control, traffic planning, and many more. A flow prediction model aims at forecasting the traffic flow of future time slices at certain regions by learning the historical traffic flow data and environmental information. However, due to the complicated traffic network topology and the dynamicity of traffic patterns in the real world, it is difficult to capture the multi-level spatial dependencies (e.g. global and local impacts to the traffic) and temporal dependencies (e.g. long-term and short-term impacts to the traffic). In this paper, we propose a Multi-step Coupled Graph Convolution Neural network (MCGCN) with temporal attention to capture the spatial and temporal dependencies of different levels in a traffic network, simultaneously, to predict traffic flow. First, a Multi-step Coupled Graph Convolution module (MCGC) is designed to learn the representation of a traffic network by coupling learning the relationship matrices, to capture the different levels’ information of a traffic network. Then, the traffic network information extracted by MCGC is fed into a Multi-step Coupled Graph Gated Recurrent Unit (MCGRU) module to realize the fusion of traffic network information and temporal features. Finally, a Multi-step Coupled Graph Attention mechanism (MCGCAtt) is used to extract the temporal information of historical time steps to predict the future traffic flow. The experiments are conducted on the NYCTaxi and NYCBike datasets, and the evaluation results demonstrate that our proposed model performs better than the nine compared methods.https://ieeexplore.ieee.org/document/9766337/Graph convolutional networkmulti-step attentiontraffic flow prediction |
| spellingShingle | Xiaohui Huang Yuming Ye Xiaofei Yang Liyan Xiong Multistep Coupled Graph Convolution With Temporal-Attention for Traffic Flow Prediction IEEE Access Graph convolutional network multi-step attention traffic flow prediction |
| title | Multistep Coupled Graph Convolution With Temporal-Attention for Traffic Flow Prediction |
| title_full | Multistep Coupled Graph Convolution With Temporal-Attention for Traffic Flow Prediction |
| title_fullStr | Multistep Coupled Graph Convolution With Temporal-Attention for Traffic Flow Prediction |
| title_full_unstemmed | Multistep Coupled Graph Convolution With Temporal-Attention for Traffic Flow Prediction |
| title_short | Multistep Coupled Graph Convolution With Temporal-Attention for Traffic Flow Prediction |
| title_sort | multistep coupled graph convolution with temporal attention for traffic flow prediction |
| topic | Graph convolutional network multi-step attention traffic flow prediction |
| url | https://ieeexplore.ieee.org/document/9766337/ |
| work_keys_str_mv | AT xiaohuihuang multistepcoupledgraphconvolutionwithtemporalattentionfortrafficflowprediction AT yumingye multistepcoupledgraphconvolutionwithtemporalattentionfortrafficflowprediction AT xiaofeiyang multistepcoupledgraphconvolutionwithtemporalattentionfortrafficflowprediction AT liyanxiong multistepcoupledgraphconvolutionwithtemporalattentionfortrafficflowprediction |