| 摘要: |
| 交通流预测是智能交通系统(intelligent transportation system, ITS)中交通管理的重要基础和热门研究方向. 传统的交通流预测方法通常需要借助大量高质量历史观测数据进行预测, 而针对更为普遍的数据稀缺的交通路网场景预测精度则急剧下降. 针对这一问题, 提出一种基于时空图卷积网络的迁移学习模型(transfer learning based on spatial-temporal graph convolutional network, TL-STGCN), 结合数据充足的源路网的交通流特征, 辅助预测数据稀缺的目标路网未来交通流. 首先, 采用基于时间注意力的时空图卷积网络学习源路网和目标路网交通流数据的时空特征表示; 其次, 结合迁移学习方法, 提取两个路网特征表示的域不变时空特征; 最后, 利用这些域不变时空特征对目标路网未来交通流做出预测. 为了验证模型的有效性, 在真实世界数据集上进行实验. 结果表明, 与现有方法对比, TL-STGCN在平均绝对误差、均方根误差以及平均绝对百分比误差指标中均取得最高精度, 证明对于数据稀缺的交通路网预测任务, TL-STGCN具有更好的预测性能. |
| 关键词: 智能交通 交通流预测 图卷积网络 注意力机制 迁移学习 |
| DOI：10.13328/j.cnki.jos.007239 |
| 分类号: |
| 基金项目:国家自然科学基金(62301099, 62071077); 中国博士后科学基金(2023MD734137); 重庆市自然科学基金创新发展联合基金(2022NSCQ-LZX0191); 重庆市教委科学技术研究项目(KJQN202300638) |
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| Intelligent Traffic Flow Prediction for Data Scarcity Scenarios |
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LI Yun1,2, GAO Ya1, YAO Zhi-Xiu2,3, XIA Shi-Chao3, WU Guang-Fu3
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1.School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;2.School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;3.School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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| Abstract: |
| Traffic flow prediction is an important foundation and a hot research direction for traffic management in intelligent transportation systems (ITS). Traditional methods for traffic flow prediction typically rely on a large amount of high-quality historical observation data to achieve accurate predictions, but the prediction accuracy significantly decreases in more common scenarios with data scarcity in traffic networks. To address this problem, a transfer learning model is proposed based on spatial-temporal graph convolutional networks (TL-STGCN), which leverages traffic flow features from a source network with abundant data to assist in predicting future traffic flow in a target network with data scarcity. Firstly, a spatial-temporal graph convolutional network based on time attention is employed to learn the spatial and temporal features of the traffic flow data in both the source and target networks. Secondly, domain-invariant spatial-temporal features are extracted from the representations of the two networks using transfer learning techniques. Lastly, these domain-invariant features are utilized to predict the future traffic flow in the target network. To validate the effectiveness of the proposed model, experiments are conducted on real-world datasets. The results demonstrate that TL-STGCN outperforms existing methods by achieving the highest accuracy in mean absolute error, root mean square error, and mean absolute percentage error, which proves that TL-STGCN provides more accurate traffic flow predictions for scenarios with data scarcity in traffic networks. |
| Key words: intelligent transportation traffic flow prediction graph convolutional network (GCN) attention mechanism transfer learning |
