Correlation Analysis for Tensor-based Traffic Data Imputation Method

被引:17
|
作者
Tan, Huachun [1 ]
Yang, Zhongxing [1 ]
Feng, Guangdong [1 ]
Wang, Wuhong [1 ]
Ran, Bin [2 ]
机构
[1] Beijing Inst Technol, Dept Transportat Engn, Beijing 100081, Peoples R China
[2] Univ Wisconsin, Dept Civil & Environm Engn, Madison, WI 53706 USA
基金
中国国家自然科学基金; 北京市自然科学基金;
关键词
tensor completion; traffic data imputation; principal component analysis; single value decomposition;
D O I
10.1016/j.sbspro.2013.08.292
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
The phenomenon of missing data in traffic has a great impact on the performance of Intelligent Transportation System (ITS). Many imputation methods have been proposed to estimate the missing traffic data. Recently, a tensor-based traffic volume imputation method has been proposed. In this paper, we focus on the underlying mechanism of tensor-based method from the viewpoint of intrinsic multi-correlations/principle components of the traffic data, and try to recommend suitable tensor pattern for traffic volume imputation. Experiments on PeMS database show that the tensor-based method outperforms matrix-based methods, and using the recommended tensor pattern achieves better performances. (C) 2013 The Authors. Published by Elsevier Ltd.
引用
收藏
页码:2611 / 2620
页数:10
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