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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/101214

    Title: A Functional Data Approach to Missing Value Imputation and Outlier Detection for Traffic Flow Data
    Authors: Chiou, Jeng-Min;Zhang, Yi-Chen;Chen, Wan-Hui;Chang, Chiung-Wen
    Contributors: 淡江大學運輸管理學系
    Keywords: functional data;functional principal component analysis;intelligent transportation system;traffic flow rate;vehicle detector
    Date: 2014
    Issue Date: 2015-04-15 20:55:46 (UTC+8)
    Publisher: Abingdon: Taylor & Francis
    Abstract: Missing values and outliers are frequently encountered in traffic monitoring data. We approach these problems by sampling the daily traffic flow rate trajectories from random functions and taking advantage of the data features using functional data analysis. We propose to impute missing values by using the conditional expectation approach to functional principal component analysis (FPCA). Our simulation study shows that the FPCA approach performs better than two commonly discussed methods in the literature, the probabilistic principal component analysis (PCA) and the Bayesian PCA, which have been shown to perform better than many conventional approaches. Based on the FPCA approach, the functional principal component scores can be applied to the functional bagplot and functional highest density region boxplot, which makes outlier detection possible for incomplete functional data. Our numerical results indicate that these two outlier detection approaches coupled with the proposed missing value imputation method can perform reasonably well. Although motivated by traffic flow data application, the proposed functional data methods for missing value imputation and outlier detection can be used in many applications with longitudinally recorded functional data.
    Relation: Transportmetrica B: Transport Dynamics 2(2), pp.106-129
    Appears in Collections:[運輸管理學系暨研究所] 期刊論文

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