Dual Graph for Traffic Forecasting
Traffic forecasting is the task of predicting future traffic based on historical traffic data. It is challenging due to the complex spatial-temporal correlation on road networks. Most existing research works use sequential Graph Neural Networks (GNN) to model traffic inference. However, they only fo...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8928590/ |
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| author | Long Wei Zhengxu Yu Zhongming Jin Liang Xie Jianqiang Huang Deng Cai Xiaofei He Xian-Sheng Hua |
| author_facet | Long Wei Zhengxu Yu Zhongming Jin Liang Xie Jianqiang Huang Deng Cai Xiaofei He Xian-Sheng Hua |
| author_sort | Long Wei |
| collection | DOAJ |
| description | Traffic forecasting is the task of predicting future traffic based on historical traffic data. It is challenging due to the complex spatial-temporal correlation on road networks. Most existing research works use sequential Graph Neural Networks (GNN) to model traffic inference. However, they only focus on nodes (intersections) or edges (road segments) traffic forecasting alone. As a result, they could hardly provide a complete description of future traffic on road networks. Actually, nodes and edges traffic are interrelated. Both of them are important for traffic safety and efficiency, and neither one is negligible. In this paper, we exploit nodes and edges information together and make traffic forecasting on nodes and edges simultaneously. We propose a novel dual graph framework, called DualGraph, to model the propagation behavior of traffic on road networks. Inside our framework, we develop a DualMap block to simulate the recursive interactions between nodes and edges. The interaction process is realized by a message passing mechanism of nearby information flow. We employ the Simulation of Urban MObility (SUMO) software to generate real-world traffic data to illustrate the effectiveness of our method. We also empirically evaluate our model on public traffic datasets. The results show that even for node or edge traffic forecasting alone, our model still outperforms compared ones, especially for long term (one hour) prediction. |
| format | Article |
| id | doaj-art-a74e186d27ab4aee9726fb6aa18ca36f |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a74e186d27ab4aee9726fb6aa18ca36f2025-08-20T02:45:49ZengIEEEIEEE Access2169-35362025-01-011312228512229310.1109/ACCESS.2019.29583808928590Dual Graph for Traffic ForecastingLong Wei0https://orcid.org/0000-0003-4021-1083Zhengxu Yu1https://orcid.org/0000-0002-6059-0300Zhongming Jin2https://orcid.org/0000-0002-2341-2978Liang Xie3https://orcid.org/0000-0002-7604-1410Jianqiang Huang4https://orcid.org/0000-0001-5735-2910Deng Cai5https://orcid.org/0000-0001-9817-4065Xiaofei He6https://orcid.org/0000-0002-3821-5125Xian-Sheng Hua7https://orcid.org/0000-0002-8232-5049State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou, ChinaState Key Laboratory of CAD&CG, Zhejiang University, Hangzhou, ChinaAlibaba Damo Academy, Alibaba Group, Hangzhou, ChinaState Key Laboratory of CAD&CG, Zhejiang University, Hangzhou, ChinaAlibaba Damo Academy, Alibaba Group, Hangzhou, ChinaState Key Laboratory of CAD&CG, Zhejiang University, Hangzhou, ChinaState Key Laboratory of CAD&CG, Zhejiang University, Hangzhou, ChinaAlibaba Damo Academy, Alibaba Group, Hangzhou, ChinaTraffic forecasting is the task of predicting future traffic based on historical traffic data. It is challenging due to the complex spatial-temporal correlation on road networks. Most existing research works use sequential Graph Neural Networks (GNN) to model traffic inference. However, they only focus on nodes (intersections) or edges (road segments) traffic forecasting alone. As a result, they could hardly provide a complete description of future traffic on road networks. Actually, nodes and edges traffic are interrelated. Both of them are important for traffic safety and efficiency, and neither one is negligible. In this paper, we exploit nodes and edges information together and make traffic forecasting on nodes and edges simultaneously. We propose a novel dual graph framework, called DualGraph, to model the propagation behavior of traffic on road networks. Inside our framework, we develop a DualMap block to simulate the recursive interactions between nodes and edges. The interaction process is realized by a message passing mechanism of nearby information flow. We employ the Simulation of Urban MObility (SUMO) software to generate real-world traffic data to illustrate the effectiveness of our method. We also empirically evaluate our model on public traffic datasets. The results show that even for node or edge traffic forecasting alone, our model still outperforms compared ones, especially for long term (one hour) prediction.https://ieeexplore.ieee.org/document/8928590/Graph neural networkstraffic forecastingtime series regression |
| spellingShingle | Long Wei Zhengxu Yu Zhongming Jin Liang Xie Jianqiang Huang Deng Cai Xiaofei He Xian-Sheng Hua Dual Graph for Traffic Forecasting IEEE Access Graph neural networks traffic forecasting time series regression |
| title | Dual Graph for Traffic Forecasting |
| title_full | Dual Graph for Traffic Forecasting |
| title_fullStr | Dual Graph for Traffic Forecasting |
| title_full_unstemmed | Dual Graph for Traffic Forecasting |
| title_short | Dual Graph for Traffic Forecasting |
| title_sort | dual graph for traffic forecasting |
| topic | Graph neural networks traffic forecasting time series regression |
| url | https://ieeexplore.ieee.org/document/8928590/ |
| work_keys_str_mv | AT longwei dualgraphfortrafficforecasting AT zhengxuyu dualgraphfortrafficforecasting AT zhongmingjin dualgraphfortrafficforecasting AT liangxie dualgraphfortrafficforecasting AT jianqianghuang dualgraphfortrafficforecasting AT dengcai dualgraphfortrafficforecasting AT xiaofeihe dualgraphfortrafficforecasting AT xianshenghua dualgraphfortrafficforecasting |