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作者:

Zhang, Yong (Zhang, Yong.) (学者:张勇) | Wei, Xiulan (Wei, Xiulan.) | Zhang, Xinyu (Zhang, Xinyu.) | Hu, Yongli (Hu, Yongli.) | Yin, Baocai (Yin, Baocai.)

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EI Scopus SCIE

摘要:

Complete and accurate traffic data is critical in urban traffic management, planning and operation. In fact, real-world traffic data contains missing values due to multiple factors, such as device outages and communication errors. For traffic data completion task, most of the existing methods are matrix/tensor completion methods, which usually enforce low rank constraint on traffic data matrix/tensor. But they neglect the graph structure of traffic data, resulting in low completion performance. Recently, graph convolutional networks have achieved remarkable results in traffic data forecasting due to their abilities of feature extraction and nonlinear fitting on arbitrarily graph-structured data. However, there are few studies based on graph neural networks for traffic data completion task. In this paper, we propose a traffic data completion model based on graph convolutional network model to impute missing values from the perspective of deep learning. This model utilizes graph convolution to model the local spatial dependency. As for global spatial dependency and temporal dependency, this model incorporates self-attention mechanism, which is applied in the spatial and temporal dimensions respectively. The experimental results on the two real-time datasets demonstrate that the proposed model outperforms the baseline methods significantly under arbitrarily missing scenarios.

关键词:

Correlation Topology Tensors Graph convolution Data models Convolution traffic data completion Task analysis self-attention mechanism Roads

作者机构:

  • [ 1 ] [Zhang, Yong]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
  • [ 2 ] [Zhang, Xinyu]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
  • [ 3 ] [Hu, Yongli]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
  • [ 4 ] [Yin, Baocai]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
  • [ 5 ] [Wei, Xiulan]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Coll Metropolitan Transportat, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China

通讯作者信息:

  • [Yin, Baocai]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China;;

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来源 :

IEEE TRANSACTIONS ON BIG DATA

ISSN: 2332-7790

年份: 2023

期: 2

卷: 9

页码: 528-541

7 . 2 0 0

JCR@2022

被引次数:

WoS核心集被引频次:

SCOPUS被引频次: 17

ESI高被引论文在榜: 0 展开所有

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