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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
ISSN/eISSN1551-3203
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
DOI10.1109/TII.2020.3003133
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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
被引频次:23[WOS]   [WOS记录]     [WOS相关记录]
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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