发表状态 | 已发表Published |
题名 | Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber-Physical Systems |
作者 | |
发表日期 | 2021-05-01 |
发表期刊 | IEEE Transactions on Industrial Informatics
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ISSN/eISSN | 1551-3203 |
卷号 | 17期号:5页码:3431-3440 |
摘要 | Recently, traffic flow prediction has drawn significant attention because it is a prerequisite in intelligent transportation management in urban informatics. The massively available traffic data collected from various sensors in transportation cyber-physical systems brings the opportunities in accurately forecasting traffic trend. Recent advances in deep learning shows the effectiveness on traffic flow prediction though most of them only demonstrate the superior performance on traffic data from a single type of vehicular carriers (e.g., cars) and does not perform well in other types of vehicles. To fill this gap, in this article, we propose a wide-attention and deep-composite (WADC) model, consisting of a wide-attention module and a deep-composite module, in this article. In particular, the wide-attention module can extract global key features from traffic flows via a linear model with self-attention mechanism. The deep-composite module can generalize local key features via convolutional neural network component and long short-term memory network component. We also perform extensive experiments on different types of traffic flow datasets to investigate the performance of WADC model. Our experimental results exhibit that WADC model outperforms other existing approaches. |
关键词 | Cyber-physical systems intelligent systems transportation vehicles |
DOI | 10.1109/TII.2020.3003133 |
URL | 查看来源 |
收录类别 | SCIE |
语种 | 英语English |
WOS研究方向 | Automation & Control Systems ; Computer Science ; Engineering |
WOS类目 | Automation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial |
WOS记录号 | WOS:000622100800043 |
Scopus入藏号 | 2-s2.0-85101774750 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://repository.uic.edu.cn/handle/39GCC9TT/7043 |
专题 | 个人在本单位外知识产出 |
作者单位 | 1.Macau University of Science and Technology,999078,Macao 2.Department of Computer Science,Norwegian University of Science and Technology,Gjovik,7491,Norway 3.College of Computer Science and Technology,Huaqiao University,Xiamen,362021,China |
推荐引用方式 GB/T 7714 | Zhou, Junhao,Dai, Hong Ning,Wang, Haoet al. Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber-Physical Systems[J]. IEEE Transactions on Industrial Informatics, 2021, 17(5): 3431-3440. |
APA | Zhou, Junhao, Dai, Hong Ning, Wang, Hao, & Wang, Tian. (2021). Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber-Physical Systems. IEEE Transactions on Industrial Informatics, 17(5), 3431-3440. |
MLA | Zhou, Junhao,et al."Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber-Physical Systems". IEEE Transactions on Industrial Informatics 17.5(2021): 3431-3440. |
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