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
相关论文
共 50 条
  • [1] A tensor-based method for missing traffic data completion
    Tan, Huachun
    Feng, Guangdong
    Feng, Jianshuai
    Wang, Wuhong
    Zhang, Yu-Jin
    Li, Feng
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2013, 28 : 15 - 27
  • [2] Traffic Speed Data Imputation Method Based on Tensor Completion
    Ran, Bin
    Tan, Huachun
    Feng, Jianshuai
    Liu, Ying
    Wang, Wuhong
    [J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2015, 2015
  • [3] A Correlation Based Imputation Method for Incomplete Traffic Accident Data
    Deb, Rupam
    Liew, Alan Wee-Chung
    Oh, Erwin
    [J]. PRICAI 2014: TRENDS IN ARTIFICIAL INTELLIGENCE, 2014, 8862 : 905 - 912
  • [4] A Tensor-Based Method for Geosensor Data Forecasting
    Zhou, Lihua
    Du, Guowang
    Xiao, Qing
    Wang, Lizhen
    [J]. WEB AND BIG DATA (APWEB-WAIM 2018), PT II, 2018, 10988 : 306 - 313
  • [5] A Tensor-Based Method for Completion of Missing Electromyography Data
    Akmal, Muhammad
    Zubair, Syed
    Jochumsen, Mads
    Kamavuako, Ernest Nlandu
    Niazi, Imran Khan
    [J]. IEEE ACCESS, 2019, 7 : 104710 - 104720
  • [6] GLOSS: Tensor-based anomaly detection in spatiotemporal urban traffic data
    Sofuoglu, Seyyid Emre
    Aviyente, Selin
    [J]. SIGNAL PROCESSING, 2022, 192
  • [7] Kernelization of Tensor-Based Models for Multiway Data Analysis
    Zhao, Qibin
    Zhou, Guoxu
    Adali, Tulay
    Zhang, Liqing
    Cichocki, Andrzej
    [J]. IEEE SIGNAL PROCESSING MAGAZINE, 2013, 30 (04) : 137 - 148
  • [8] Tensor-Based Classification Models for Hyperspectral Data Analysis
    Makantasis, Konstantinos
    Doulamis, Anastasios D.
    Doulamis, Nikolaos D.
    Nikitakis, Antonis
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2018, 56 (12): : 6884 - 6898
  • [9] A tensor-based K-nearest neighbors method for traffic speed prediction under data missing
    Zheng, Liang
    Huang, Huimin
    Zhu, Chuang
    Zhang, Kunpeng
    [J]. TRANSPORTMETRICA B-TRANSPORT DYNAMICS, 2020, 8 (01) : 182 - 199
  • [10] Spatial–temporal regularized tensor decomposition method for traffic speed data imputation
    Haojie Xie
    Yongshun Gong
    Xiangjun Dong
    [J]. International Journal of Data Science and Analytics, 2024, 17 : 203 - 223