The missing data problem greatly affects traffic analysis. In this paper, we put forward a new reliable method called probabilistic principal component analysis (PPCA) to impute the missing flow volume data based on historical data mining. First, we review the current missing data-imputation method and why it may fail to yield acceptable results in many traffic flow applications. Second, we examine the statistical properties of traffic flow volume time series. We show that the fluctuations of traffic flow are Gaussian type and that principal component analysis (PCA) can be used to retrieve the features of traffic flow. Third, we discuss how to use a robust PCA to filter out the abnormal traffic flow data that disturb the imputation process. Finally, we recall the theories of PPCA/Bayesian PCA-based imputation algorithms and compare their performance with some conventional methods, including the nearest/mean historical imputation methods and the local interpolation/regression methods. The experiments prove that the PPCA method provides significantly better performance than the conventional methods, reducing the root-mean-square imputation error by at least 25%.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    PPCA-Based Missing Data Imputation for Traffic Flow Volume: A Systematical Approach


    Contributors:
    Li Qu, (author) / Jianming Hu, (author) / Li Li, (author) / Yi Zhang, (author)


    Publication date :

    2009-09-01


    Size :

    1560416 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Missing traffic data: comparison of imputation methods

    Li, Yuebiao / Li, Zhiheng / Li, Li | Wiley | 2014

    Free access

    Missing traffic data: comparison of imputation methods

    Li, Yuebiao / Li, Zhiheng / Li, Li | IET | 2014

    Free access

    A clustering-based approach for data-driven imputation of missing traffic data

    Ku, Wei Chiet / Jagadeesh, George R. / Prakash, Alok et al. | IEEE | 2016


    A Comprehensive Survey on Traffic Missing Data Imputation

    Zhang, Yimei / Kong, Xiangjie / Zhou, Wenfeng et al. | IEEE | 2024