Spatial-Similarity Dynamic Graph Bidirectional Double-Cell Network for Traffic Flow Prediction
Accurate traffic flow prediction plays a pivotal role in optimizing urban transportation systems and improving traffic management efficacy. To address the limitations of existing methods in modeling complex spatial-temporal dependencies within dynamic traffic networks, this paper introduces a Spatia...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11031461/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849340399461597184 |
|---|---|
| author | Zhifei Yang Zeyang Li Jia Zhang |
| author_facet | Zhifei Yang Zeyang Li Jia Zhang |
| author_sort | Zhifei Yang |
| collection | DOAJ |
| description | Accurate traffic flow prediction plays a pivotal role in optimizing urban transportation systems and improving traffic management efficacy. To address the limitations of existing methods in modeling complex spatial-temporal dependencies within dynamic traffic networks, this paper introduces a Spatial-Similarity Dynamic Graph Bidirectional Double-Cell Network (SDGBDCN). The proposed architecture incorporates two innovative components: 1) a Spatial Similarity Dynamic Graph Convolution (SDGCN) module that adaptively aggregates spatial features through node similarity analysis and time-varying graph structures, and 2) a Bidirectional Double-Cell Recurrent Neural Network (Bi-DouCRNN) that combines LSTM and GRU mechanisms via dual-gating operations to capture multi-scale temporal dynamics. Comprehensive evaluations on PeMS datasets demonstrate superior performance compared to existing approaches. Statistical validation through AIC and SBIC metrics confirms the model’s exceptional capability, achieving record-low scores of 1204.2 and 1219.8 respectively. This research advances traffic prediction methodologies through its integrated approach to dynamic spatial correlation modeling and bidirectional temporal learning, providing valuable insights for intelligent transportation system development. |
| format | Article |
| id | doaj-art-a2b4f20b6afe46d8a812a34cdc1babc7 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a2b4f20b6afe46d8a812a34cdc1babc72025-08-20T03:43:55ZengIEEEIEEE Access2169-35362025-01-011310684910686210.1109/ACCESS.2025.357913811031461Spatial-Similarity Dynamic Graph Bidirectional Double-Cell Network for Traffic Flow PredictionZhifei Yang0Zeyang Li1Jia Zhang2School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, ChinaSchool of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, ChinaSchool of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, ChinaAccurate traffic flow prediction plays a pivotal role in optimizing urban transportation systems and improving traffic management efficacy. To address the limitations of existing methods in modeling complex spatial-temporal dependencies within dynamic traffic networks, this paper introduces a Spatial-Similarity Dynamic Graph Bidirectional Double-Cell Network (SDGBDCN). The proposed architecture incorporates two innovative components: 1) a Spatial Similarity Dynamic Graph Convolution (SDGCN) module that adaptively aggregates spatial features through node similarity analysis and time-varying graph structures, and 2) a Bidirectional Double-Cell Recurrent Neural Network (Bi-DouCRNN) that combines LSTM and GRU mechanisms via dual-gating operations to capture multi-scale temporal dynamics. Comprehensive evaluations on PeMS datasets demonstrate superior performance compared to existing approaches. Statistical validation through AIC and SBIC metrics confirms the model’s exceptional capability, achieving record-low scores of 1204.2 and 1219.8 respectively. This research advances traffic prediction methodologies through its integrated approach to dynamic spatial correlation modeling and bidirectional temporal learning, providing valuable insights for intelligent transportation system development.https://ieeexplore.ieee.org/document/11031461/Deep neural networkstraffic flow predictionLSTMGRU |
| spellingShingle | Zhifei Yang Zeyang Li Jia Zhang Spatial-Similarity Dynamic Graph Bidirectional Double-Cell Network for Traffic Flow Prediction IEEE Access Deep neural networks traffic flow prediction LSTM GRU |
| title | Spatial-Similarity Dynamic Graph Bidirectional Double-Cell Network for Traffic Flow Prediction |
| title_full | Spatial-Similarity Dynamic Graph Bidirectional Double-Cell Network for Traffic Flow Prediction |
| title_fullStr | Spatial-Similarity Dynamic Graph Bidirectional Double-Cell Network for Traffic Flow Prediction |
| title_full_unstemmed | Spatial-Similarity Dynamic Graph Bidirectional Double-Cell Network for Traffic Flow Prediction |
| title_short | Spatial-Similarity Dynamic Graph Bidirectional Double-Cell Network for Traffic Flow Prediction |
| title_sort | spatial similarity dynamic graph bidirectional double cell network for traffic flow prediction |
| topic | Deep neural networks traffic flow prediction LSTM GRU |
| url | https://ieeexplore.ieee.org/document/11031461/ |
| work_keys_str_mv | AT zhifeiyang spatialsimilaritydynamicgraphbidirectionaldoublecellnetworkfortrafficflowprediction AT zeyangli spatialsimilaritydynamicgraphbidirectionaldoublecellnetworkfortrafficflowprediction AT jiazhang spatialsimilaritydynamicgraphbidirectionaldoublecellnetworkfortrafficflowprediction |