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Traffic Volume Data Outlier Recovery via Tensor Model

机译：通过Tensor模型恢复流量数据离群值

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摘要

Traffic volume data is already collected and used for a variety of purposes in intelligent transportation system (ITS). However, the collected data might be abnormal due to the problem of outlier data caused by malfunctions in data collection and record systems. To fully analyze and operate the collected data, it is necessary to develop a validate method for addressing the outlier data. Many existing algorithms have studied the problem of outlier recovery based on the time series methods. In this paper, a multiway tensor model is proposed for constructing the traffic volume data based on the intrinsic multilinear correlations, such as day to day and hour to hour. Then, a novel tensor recovery method, called ADMM-TR, is proposed for recovering outlier data of traffic volume data. The proposed method is evaluated on synthetic data and real world traffic volume data. Experimental results demonstrate the practicability, effectiveness, and advantage of the proposed method, especially for the real world traffic volume data.
机译：交通量数据已被收集并用于智能运输系统（ITS）中的多种用途。但是，由于数据收集和记录系统中的故障导致异常数据的问题，收集的数据可能会异常。为了完全分析和操作收集的数据，有必要开发一种验证方法来处理异常数据。现有的许多算法已经研究了基于时间序列方法的离群值恢复问题。本文提出了一种基于内在的多线性相关性（如日与日，小时与小时）构建交通量数据的多张量模型。然后，提出了一种新的张量恢复方法，称为ADMM-TR，用于恢复流量数据的异常数据。该方法对合成数据和现实交通量数据进行了评估。实验结果证明了该方法的实用性，有效性和优势，特别是对于现实世界的交通量数据。

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  • 来源
    《Mathematical Problems in Engineering》 |2013年第3期|164810.1-164810.8|共8页
  • 作者

    Huachun Tan; Jianshuai Feng; Guangdong Feng; Wuhong Wang; Yu-Jin Zhang;

  • 作者单位

    Department of Transportation Engineering, Beijing Institute of Technology, Beijing 100081, China;

    Integrated Information System Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;

    Civil Aviation Engineering Consulting Company of China, Beijing 100621, China;

    Department of Transportation Engineering, Beijing Institute of Technology, Beijing 100081, China;

    Department of Electronic Engineering, Tsinghua University, Beijing 100084, China;

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