A functional data approach to missing value imputation and outlier detection for traffic flow data
Journal
Transportmetrica B: Transport Dynamics
Journal Volume
2
Journal Issue
2
Start Page
106-129
ISSN
2168-0566
2168-0582
Date Issued
2014-03-14
Author(s)
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. © 2014 Hong Kong Society for Transportation Studies Limited.
Subjects
functional data
functional principal component analysis
intelligent transportation system
traffic flow rate
vehicle detector
Publisher
Informa UK Limited
Type
journal article