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Missing Traffic Data Imputation with a Linear Generative Model Based on Probabilistic Principal Component Analysis
Even with the ubiquitous sensing data in intelligent transportation systems, such as the mobile sensing of vehicle trajectories, traffic estimation is still faced with the data missing problem due to the detector faults or limited number of probe vehicles as mobile sensors. Such data missing issue p...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824200/ https://www.ncbi.nlm.nih.gov/pubmed/36616802 http://dx.doi.org/10.3390/s23010204 |
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author | Huang, Liping Li, Zhenghuan Luo, Ruikang Su, Rong |
author_facet | Huang, Liping Li, Zhenghuan Luo, Ruikang Su, Rong |
author_sort | Huang, Liping |
collection | PubMed |
description | Even with the ubiquitous sensing data in intelligent transportation systems, such as the mobile sensing of vehicle trajectories, traffic estimation is still faced with the data missing problem due to the detector faults or limited number of probe vehicles as mobile sensors. Such data missing issue poses an obstacle for many further explorations, e.g., the link-based traffic status modeling. Although many studies have focused on tackling this kind of problem, existing studies mainly focus on the situation in which data are missing at random and ignore the distinction between links of missing data. In the practical scenario, traffic speed data are always missing not at random (MNAR). The distinction for recovering missing data on different links has not been studied yet. In this paper, we propose a general linear model based on probabilistic principal component analysis (PPCA) for solving MNAR traffic speed data imputation. Furthermore, we propose a metric, i.e., Pearson score (p-score), for distinguishing links and investigate how the model performs on links with different p-score values. Experimental results show that the new model outperforms the typically used PPCA model, and missing data on links with higher p-score values can be better recovered. |
format | Online Article Text |
id | pubmed-9824200 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98242002023-01-08 Missing Traffic Data Imputation with a Linear Generative Model Based on Probabilistic Principal Component Analysis Huang, Liping Li, Zhenghuan Luo, Ruikang Su, Rong Sensors (Basel) Article Even with the ubiquitous sensing data in intelligent transportation systems, such as the mobile sensing of vehicle trajectories, traffic estimation is still faced with the data missing problem due to the detector faults or limited number of probe vehicles as mobile sensors. Such data missing issue poses an obstacle for many further explorations, e.g., the link-based traffic status modeling. Although many studies have focused on tackling this kind of problem, existing studies mainly focus on the situation in which data are missing at random and ignore the distinction between links of missing data. In the practical scenario, traffic speed data are always missing not at random (MNAR). The distinction for recovering missing data on different links has not been studied yet. In this paper, we propose a general linear model based on probabilistic principal component analysis (PPCA) for solving MNAR traffic speed data imputation. Furthermore, we propose a metric, i.e., Pearson score (p-score), for distinguishing links and investigate how the model performs on links with different p-score values. Experimental results show that the new model outperforms the typically used PPCA model, and missing data on links with higher p-score values can be better recovered. MDPI 2022-12-25 /pmc/articles/PMC9824200/ /pubmed/36616802 http://dx.doi.org/10.3390/s23010204 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Huang, Liping Li, Zhenghuan Luo, Ruikang Su, Rong Missing Traffic Data Imputation with a Linear Generative Model Based on Probabilistic Principal Component Analysis |
title | Missing Traffic Data Imputation with a Linear Generative Model Based on Probabilistic Principal Component Analysis |
title_full | Missing Traffic Data Imputation with a Linear Generative Model Based on Probabilistic Principal Component Analysis |
title_fullStr | Missing Traffic Data Imputation with a Linear Generative Model Based on Probabilistic Principal Component Analysis |
title_full_unstemmed | Missing Traffic Data Imputation with a Linear Generative Model Based on Probabilistic Principal Component Analysis |
title_short | Missing Traffic Data Imputation with a Linear Generative Model Based on Probabilistic Principal Component Analysis |
title_sort | missing traffic data imputation with a linear generative model based on probabilistic principal component analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824200/ https://www.ncbi.nlm.nih.gov/pubmed/36616802 http://dx.doi.org/10.3390/s23010204 |
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