Knowledge Commons of Institute of Automation,CAS
A Multi-Stream Feature Fusion Approach for Traffic Prediction | |
Zhishuai Li1; Gang Xiong1; Yonglin Tian1; Yisheng Lv1; Yuanyuan Chen1; Pan Hui2; Xiang Su3 | |
发表期刊 | IEEE Transactions on Intelligent Transportation Systems |
2022 | |
卷号 | 23期号:2页码:1456-1466 |
摘要 | Accurate and timely traffic flow prediction is crucial for intelligent transportation systems (ITS). Recent advances in graph-based neural networks have achieved promising prediction results. However, some challenges remain, especially regarding graph construction and the time complexity of models. In this paper, we propose a multi-stream feature fusion approach to extract and integrate rich features from traffic data and leverage a data-driven adjacent matrix instead of the distance-based matrix to construct graphs. We calculate the Spearman rank correlation coefficient between monitor stations to obtain the initial adjacent matrix and fine-tune it while training. As to the model, we construct a multi-stream feature fusion block (MFFB) module, which includes a three-channel network and the soft-attention mechanism. The three-channel networks are graph convolutional neural network (GCN), gated recurrent unit (GRU) and fully connected neural network (FNN), which are used to extract spatial, temporal and other features, respectively. The soft-attention mechanism is utilized to integrate the obtained features. The MFFB modules are stacked, and a fully connected layer and a convolutional layer are used to make predictions. We conduct experiments on two real-world traffic prediction tasks and verify that our proposed approach outperforms the state-of-the-art methods within an acceptable time complexity. |
关键词 | Traffic prediction, graph convolutional neural network, deep learning, multi-stream |
收录类别 | SCI |
语种 | 英语 |
WOS记录号 | WOS:000750200400063 |
七大方向——子方向分类 | 人工智能+交通 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/40596 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Yisheng Lv |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.The Hong Kong University of Science and Technology 3.University of Helsinki 4.University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhishuai Li,Gang Xiong,Yonglin Tian,et al. A Multi-Stream Feature Fusion Approach for Traffic Prediction[J]. IEEE Transactions on Intelligent Transportation Systems,2022,23(2):1456-1466. |
APA | Zhishuai Li.,Gang Xiong.,Yonglin Tian.,Yisheng Lv.,Yuanyuan Chen.,...&Xiang Su.(2022).A Multi-Stream Feature Fusion Approach for Traffic Prediction.IEEE Transactions on Intelligent Transportation Systems,23(2),1456-1466. |
MLA | Zhishuai Li,et al."A Multi-Stream Feature Fusion Approach for Traffic Prediction".IEEE Transactions on Intelligent Transportation Systems 23.2(2022):1456-1466. |
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