Multi-factor embedding GNN-based traffic flow prediction considering intersection similarity
Existing studies on traffic flow prediction primarily rely on on-board devices to collect vehicle trajectory data, which can potentially infringe upon the privacy of users and limit the applicability of the method. Additionally, traffic flow prediction remains challenging due to the complex spatial...
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| Vydané v: | Neurocomputing (Amsterdam) Ročník 620; s. 129193 |
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| Hlavní autori: | , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.03.2025
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| ISSN: | 0925-2312 |
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| Abstract | Existing studies on traffic flow prediction primarily rely on on-board devices to collect vehicle trajectory data, which can potentially infringe upon the privacy of users and limit the applicability of the method. Additionally, traffic flow prediction remains challenging due to the complex spatial and temporal dependencies within real-world traffic networks. To address these limitations, this paper introduces a framework for analyzing discrete vehicle trajectory data at urban intersections. By incorporating various external physical factors into traffic flow prediction, this framework derives embedding vectors from vehicle trajectory sequences and road network topology, modeling their spatio-temporal dependencies using Skip-Gram and GraphSAGE, respectively. Additionally, the intersection similarity is introduced to capture and integrate traffic flow patterns between the target intersection and similar intersections. A Spatio-Temporal Graph Convolutional Neural Network (ST-GCN) algorithm, which combines Graph Convolutional Networks (GCN) with Long Short-Term Memory (LSTM), is developed to achieve precise traffic flow prediction. Extensive experiments on a real-world traffic flow dataset from Qingdao, China, validate that the proposed method outperforms state-of-the-art baseline methods. |
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| AbstractList | Existing studies on traffic flow prediction primarily rely on on-board devices to collect vehicle trajectory data, which can potentially infringe upon the privacy of users and limit the applicability of the method. Additionally, traffic flow prediction remains challenging due to the complex spatial and temporal dependencies within real-world traffic networks. To address these limitations, this paper introduces a framework for analyzing discrete vehicle trajectory data at urban intersections. By incorporating various external physical factors into traffic flow prediction, this framework derives embedding vectors from vehicle trajectory sequences and road network topology, modeling their spatio-temporal dependencies using Skip-Gram and GraphSAGE, respectively. Additionally, the intersection similarity is introduced to capture and integrate traffic flow patterns between the target intersection and similar intersections. A Spatio-Temporal Graph Convolutional Neural Network (ST-GCN) algorithm, which combines Graph Convolutional Networks (GCN) with Long Short-Term Memory (LSTM), is developed to achieve precise traffic flow prediction. Extensive experiments on a real-world traffic flow dataset from Qingdao, China, validate that the proposed method outperforms state-of-the-art baseline methods. |
| ArticleNumber | 129193 |
| Author | Hu, Bingtao Wang, Fei Song, Xiuju Li, Zhiwu Zhong, Ruirui Tan, Jianrong Feng, Yixiong Lou, Shanhe Wang, Yong |
| Author_xml | – sequence: 1 givenname: Ruirui orcidid: 0000-0002-6761-2744 surname: Zhong fullname: Zhong, Ruirui organization: State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China – sequence: 2 givenname: Bingtao orcidid: 0000-0002-4939-8115 surname: Hu fullname: Hu, Bingtao email: hubingtao@zju.edu.cn organization: State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China – sequence: 3 givenname: Fei surname: Wang fullname: Wang, Fei organization: College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, 266000, China – sequence: 4 givenname: Yixiong surname: Feng fullname: Feng, Yixiong organization: State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China – sequence: 5 givenname: Zhiwu orcidid: 0000-0003-1955-7661 surname: Li fullname: Li, Zhiwu organization: Institute of Systems Engineering, Macau University of Science and Technology, 999078, Macao Special Administrative Region of China – sequence: 6 givenname: Xiuju surname: Song fullname: Song, Xiuju organization: State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China – sequence: 7 givenname: Yong surname: Wang fullname: Wang, Yong organization: State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China – sequence: 8 givenname: Shanhe surname: Lou fullname: Lou, Shanhe organization: State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China – sequence: 9 givenname: Jianrong surname: Tan fullname: Tan, Jianrong organization: State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China |
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| Keywords | Representation learning Traffic flow prediction Multi-factor Graph neural network Spatio-temporal modeling |
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