Functional clustering and missing value imputation of traffic flow trajectories
Journal
Transportmetrica B: Transport Dynamics
Journal Volume
9
Journal Issue
1
Start Page
1-21
ISSN
2168-0566
2168-0582
Date Issued
2020-07-21
Author(s)
Pai-Ling Li
Abstract
Patterns of traffic flow trajectories play an essential role in analysing traffic monitoring data in transportation studies. This research presents a data-adaptive clustering approach to explore traffic flow patterns and a unified algorithm to impute missing values for incomplete traffic flow trajectories. We recommend using subspace-projected functional data clustering with the assumption that each observed daily traffic flow trajectory is a realization of a random function sampled from a mixture of stochastic processes, and each subprocess represents a cluster subspace spanned by the mean function and eigenfunctions of the covariance kernel of the random trajectories. The unified algorithm combines probabilistic functional clustering with functional principal component analysis to propose a mixture prediction for missing value imputation. The proposed methods effectively unravel distinctive daily traffic flow patterns and improve the accuracy of missing value imputation. The advantage of the proposed approaches is demonstrated through numerical studies of a real traffic flow data application. © 2020, © 2020 Hong Kong Society for Transportation Studies Limited.
Subjects
Functional data analysis
missing value
principal component analysis
traffic flow rate
unsupervised learning
vehicle loop detector
Publisher
Informa UK Limited
Type
journal article