Traffic Missing Data Imputation: A Selective Overview of Temporal Theories and Algorithms
A great challenge for intelligent transportation systems (ITS) is missing traffic data. Traffic data are input from various transportation applications. In the past few decades, several methods for traffic temporal data imputation have been proposed. A key issue is that temporal information collecte...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/10/14/2544 |
| _version_ | 1797445398157066240 |
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| author | Tuo Sun Shihao Zhu Ruochen Hao Bo Sun Jiemin Xie |
| author_facet | Tuo Sun Shihao Zhu Ruochen Hao Bo Sun Jiemin Xie |
| author_sort | Tuo Sun |
| collection | DOAJ |
| description | A great challenge for intelligent transportation systems (ITS) is missing traffic data. Traffic data are input from various transportation applications. In the past few decades, several methods for traffic temporal data imputation have been proposed. A key issue is that temporal information collected by neighbor detectors can make traffic missing data imputation more accurate. This review analyzes traffic temporal data imputation methods. Research methods, missing patterns, assumptions, imputation styles, application conditions, limitations, and public datasets are reviewed. Then, five representative methods are tested under different missing patterns and missing ratios. California performance measurement system (PeMS) data including traffic volume and speed are selected to conduct the test. Probabilistic principal component analysis performs the best under the most conditions. |
| first_indexed | 2024-03-09T13:25:16Z |
| format | Article |
| id | doaj.art-5a729747db0d436da591503d2f09def0 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T13:25:16Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-5a729747db0d436da591503d2f09def02023-11-30T21:24:37ZengMDPI AGMathematics2227-73902022-07-011014254410.3390/math10142544Traffic Missing Data Imputation: A Selective Overview of Temporal Theories and AlgorithmsTuo Sun0Shihao Zhu1Ruochen Hao2Bo Sun3Jiemin Xie4Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, ChinaAnting Shanghai International Automobile City, Shanghai 201804, ChinaKey Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, ChinaDepartment of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, SingaporeSchool of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, ChinaA great challenge for intelligent transportation systems (ITS) is missing traffic data. Traffic data are input from various transportation applications. In the past few decades, several methods for traffic temporal data imputation have been proposed. A key issue is that temporal information collected by neighbor detectors can make traffic missing data imputation more accurate. This review analyzes traffic temporal data imputation methods. Research methods, missing patterns, assumptions, imputation styles, application conditions, limitations, and public datasets are reviewed. Then, five representative methods are tested under different missing patterns and missing ratios. California performance measurement system (PeMS) data including traffic volume and speed are selected to conduct the test. Probabilistic principal component analysis performs the best under the most conditions.https://www.mdpi.com/2227-7390/10/14/2544missing data imputationtime series analysismissing pattern |
| spellingShingle | Tuo Sun Shihao Zhu Ruochen Hao Bo Sun Jiemin Xie Traffic Missing Data Imputation: A Selective Overview of Temporal Theories and Algorithms Mathematics missing data imputation time series analysis missing pattern |
| title | Traffic Missing Data Imputation: A Selective Overview of Temporal Theories and Algorithms |
| title_full | Traffic Missing Data Imputation: A Selective Overview of Temporal Theories and Algorithms |
| title_fullStr | Traffic Missing Data Imputation: A Selective Overview of Temporal Theories and Algorithms |
| title_full_unstemmed | Traffic Missing Data Imputation: A Selective Overview of Temporal Theories and Algorithms |
| title_short | Traffic Missing Data Imputation: A Selective Overview of Temporal Theories and Algorithms |
| title_sort | traffic missing data imputation a selective overview of temporal theories and algorithms |
| topic | missing data imputation time series analysis missing pattern |
| url | https://www.mdpi.com/2227-7390/10/14/2544 |
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