T-RippleGNN: Predicting traffic flow through ripple propagation with attentive graph neural networks.
Recently, accurate traffic flow prediction has become a significant part of intelligent transportation systems, which can not only satisfy citizens' travel need and life satisfaction, but also benefit urban traffic management and control. However, traffic forecasting remains highly challenging...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0323787 |
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| author | Anning Ji Xintao Ma |
| author_facet | Anning Ji Xintao Ma |
| author_sort | Anning Ji |
| collection | DOAJ |
| description | Recently, accurate traffic flow prediction has become a significant part of intelligent transportation systems, which can not only satisfy citizens' travel need and life satisfaction, but also benefit urban traffic management and control. However, traffic forecasting remains highly challenging because of its complexity in both topology structure and time transformation. Inspired by the propagation idea of graph convolutional networks, we propose ripple-propagation-based attentive graph neural networks for traffic flow prediction (T-RippleGNN). Firstly, we adopt Ripple propagation to capture the topology structure of the traffic spatial model. Then, a GRU-based model is used to explore the traffic model through the timeline. Lastly, those two factors are combined and attention scores are assigned to differentiate their influences on the traffic flow prediction. Furthermore, we evaluate our approach with three real-world traffic datasets. The results show that our approach reduces the prediction errors by approximately 2.24%-62,93% compared with state-of-the-art baselines, and the effectiveness of T-RippleGNN in traffic forecasting is demonstrated. |
| format | Article |
| id | doaj-art-e14e520066c441889e27a32e25e7b36a |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-e14e520066c441889e27a32e25e7b36a2025-08-20T02:23:15ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01205e032378710.1371/journal.pone.0323787T-RippleGNN: Predicting traffic flow through ripple propagation with attentive graph neural networks.Anning JiXintao MaRecently, accurate traffic flow prediction has become a significant part of intelligent transportation systems, which can not only satisfy citizens' travel need and life satisfaction, but also benefit urban traffic management and control. However, traffic forecasting remains highly challenging because of its complexity in both topology structure and time transformation. Inspired by the propagation idea of graph convolutional networks, we propose ripple-propagation-based attentive graph neural networks for traffic flow prediction (T-RippleGNN). Firstly, we adopt Ripple propagation to capture the topology structure of the traffic spatial model. Then, a GRU-based model is used to explore the traffic model through the timeline. Lastly, those two factors are combined and attention scores are assigned to differentiate their influences on the traffic flow prediction. Furthermore, we evaluate our approach with three real-world traffic datasets. The results show that our approach reduces the prediction errors by approximately 2.24%-62,93% compared with state-of-the-art baselines, and the effectiveness of T-RippleGNN in traffic forecasting is demonstrated.https://doi.org/10.1371/journal.pone.0323787 |
| spellingShingle | Anning Ji Xintao Ma T-RippleGNN: Predicting traffic flow through ripple propagation with attentive graph neural networks. PLoS ONE |
| title | T-RippleGNN: Predicting traffic flow through ripple propagation with attentive graph neural networks. |
| title_full | T-RippleGNN: Predicting traffic flow through ripple propagation with attentive graph neural networks. |
| title_fullStr | T-RippleGNN: Predicting traffic flow through ripple propagation with attentive graph neural networks. |
| title_full_unstemmed | T-RippleGNN: Predicting traffic flow through ripple propagation with attentive graph neural networks. |
| title_short | T-RippleGNN: Predicting traffic flow through ripple propagation with attentive graph neural networks. |
| title_sort | t ripplegnn predicting traffic flow through ripple propagation with attentive graph neural networks |
| url | https://doi.org/10.1371/journal.pone.0323787 |
| work_keys_str_mv | AT anningji tripplegnnpredictingtrafficflowthroughripplepropagationwithattentivegraphneuralnetworks AT xintaoma tripplegnnpredictingtrafficflowthroughripplepropagationwithattentivegraphneuralnetworks |