Robust missing traffic flow imputation considering nonnegativity and road capacity
type: Item
instance of: scholarly article
title: Robust missing traffic flow imputation considering nonnegativity and road capacity
zbMATH Open document ID: 1407.90106
DOI: 10.1155/2014/763469
author: Xianqiang Yang
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: There are increasing concerns about missing traffic data in recent years. In this paper, a robust missing traffic flow data imputation approach based on matrix completion is proposed. In the proposed method, the similarity of traffic flow from day to day is exploited to impute missing data by the low-rank hypothesis of constructed traffic flow matrix. And the physical limitation of road capacity and nonnegativity is also considered through the optimization process, which avoids the possibility of producing negative and overcapacity values. Moreover, the proposed algorithm can impute missing data and recover outlier in a unify framework. The experiment results show that the proposed method is more accurate, stable, and reasonable.
Mathematics Subject Classification ID: 90B20
zbMATH DE Number: 7017217
Wikidata QID: Q59072032
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/763469
OpenAlex ID: W2090035883
cites work: Q4908856
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description: scientific article; zbMATH DE number 7017217