Functional clustering and missing value imputation of traffic flow trajectories

doi:10.1080/21680566.2020.1781706

淡江大學機構典藏 > 商管學院 > 統計學系暨研究所 > 期刊論文 > Item 987654321/120133

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题名:	Functional clustering and missing value imputation of traffic flow trajectories
作者:	Li, Pai-Ling;Chiou, Jeng-Min
关键词:	Functional data analysis;missing value;principal component analysis;traffic flow rate;unsupervised learning;vehicle loop detector
日期:	2020-07-21
上传时间:	2021-03-10 12:13:58 (UTC+8)
出版者:	Taylor & Francis
摘要:	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.
關聯:	Transportmetrica B: Transport Dynamics 9(1), p.1-21
DOI:	10.1080/21680566.2020.1781706
显示于类别:	[統計學系暨研究所] 期刊論文

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