The invention discloses a traffic data restoration method based on non-convex low-rank tensor representation. The method comprises the following steps: S1, constructing a traffic data third-order tensor; s2, the Capped Schatten p norm of the tensor is defined, and the Capped Schatten p norm is defined; s3, constructing an improved Laplacian regular term; and S4, solving an optimization problem by using an iteration mode until a termination condition is met. According to the method, a Capped Schatten p norm of a tensor is used as a non-convex approximation of a rank, so that the inherent low-rank characteristic of traffic data is better represented; according to the method, the traffic state change characteristics of similar road sections are utilized to assist in repairing missing data, so that high-precision repairing of the traffic data is completed, the urgent requirement for high data quality in intelligent traffic system application can be met, and the method has theoretical significance and application value.

    本发明公开了一种基于非凸低秩张量表示的交通数据修复方法,包括如下步骤:S1、构造交通数据三阶张量;S2、定义张量的Capped Schatten p范数;S3、构造改进的拉普拉斯正则项;S4、使用迭代方式求解最优化问题直至满足终止条件。该方法使用张量的Capped Schatten p范数,作为秩的非凸近似,以更好地表征交通数据固有的低秩特性;并整合改进的拉普拉斯正则项作为空间平滑约束,以利用相似路段的交通状态变化特征来辅助修复缺失数据,进而完成交通数据的高精度修复,能够满足智能交通系统应用中对数据高质量的迫切需求,具有理论意义和应用价值。


    Access

    Download


    Export, share and cite



    Title :

    Traffic data restoration method based on non-convex low-rank tensor representation


    Additional title:

    一种基于非凸低秩张量表示的交通数据修复方法


    Contributors:
    HU LIYANG (author) / YE ZHIRUI (author) / HAN ZHONGYI (author) / ZHANG YUHAN (author)

    Publication date :

    2023-03-21


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



    Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation

    Chen, Xinyu / Lei, Mengying / Saunier, Nicolas et al. | IEEE | 2022


    Scalable low-rank tensor learning for spatiotemporal traffic data imputation

    Chen, Xinyu / Chen, Yixian / Saunier, Nicolas et al. | Elsevier | 2021


    Low-Rank Hankel Tensor Completion for Traffic Speed Estimation

    Wang, Xudong / Wu, Yuankai / Zhuang, Dingyi et al. | IEEE | 2023



    Traffic forecasting with missing data via low rank dynamic mode decomposition of tensor

    Zhang, Xinyu / Zhang, Yong / Wei, Xiulan et al. | Wiley | 2022

    Free access