Missing Traffic Data Imputation for Artificial Intelligence in Intelligent Transportation Systems: Review of Methods, Limitations, and Challenges
Missing data in Intelligent Transportation Systems (ITS) could lead to possible errors in the analyses of traffic data. Applying Artificial Intelligence (AI) in these circumstances can mitigate such problems. Past works focused only on specific data imputation methods, such as tensor factorization o...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10091533/ |
| _version_ | 1797849293348929536 |
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| author | Robin Kuok Cheong Chan Joanne Mun-Yee Lim Rajendran Parthiban |
| author_facet | Robin Kuok Cheong Chan Joanne Mun-Yee Lim Rajendran Parthiban |
| author_sort | Robin Kuok Cheong Chan |
| collection | DOAJ |
| description | Missing data in Intelligent Transportation Systems (ITS) could lead to possible errors in the analyses of traffic data. Applying Artificial Intelligence (AI) in these circumstances can mitigate such problems. Past works focused only on specific data imputation methods, such as tensor factorization or a specific neural network model. While there are review papers covering singular topics regarding missing data, there are none in the field of traffic, to the best of our knowledge, that introduces the process of missing data collection and the viability of the traffic data collected while also broadly covering the popularly used models of recent years. This has led to non-uniformity of the terms used in missing data imputation, limited research in areas where datasets are not available, and a narrowed view of the methods used for data imputation. Hence, this paper aims to standardize the terms used in missing data classifications, look into the limitations of using available public or private datasets for urban traffic research, and discuss popular statistical and data-driven methods used by recent AI and ITS papers. It was found that tensor decomposition-based methods are the most popular for missing data imputation, followed by Generative Adversarial Networks and Graph Neural Networks, all of which rely on a large training dataset. Meanwhile, Probability Principle Component Analysis (PPCA) methods provide valuable insights via traffic analysis and are used for real-time traffic imputation. This paper also highlights the need for more efficient and reliable methods for traffic data collection, such as online APIs. |
| first_indexed | 2024-04-09T18:41:31Z |
| format | Article |
| id | doaj.art-3446249473864cbb9138c0c5a906e8b5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T18:41:31Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3446249473864cbb9138c0c5a906e8b52023-04-10T23:01:05ZengIEEEIEEE Access2169-35362023-01-0111340803409310.1109/ACCESS.2023.326421610091533Missing Traffic Data Imputation for Artificial Intelligence in Intelligent Transportation Systems: Review of Methods, Limitations, and ChallengesRobin Kuok Cheong Chan0https://orcid.org/0000-0002-0156-8648Joanne Mun-Yee Lim1https://orcid.org/0000-0002-1326-8634Rajendran Parthiban2https://orcid.org/0000-0003-0983-9796School of Engineering, Monash University Malaysia, Bandar Sunway, Selangor, MalaysiaSchool of Engineering, Monash University Malaysia, Bandar Sunway, Selangor, MalaysiaDepartment of Electrical and Computer Systems Engineering, Faculty of Engineering, Monash University, Clayton, VIC, AustraliaMissing data in Intelligent Transportation Systems (ITS) could lead to possible errors in the analyses of traffic data. Applying Artificial Intelligence (AI) in these circumstances can mitigate such problems. Past works focused only on specific data imputation methods, such as tensor factorization or a specific neural network model. While there are review papers covering singular topics regarding missing data, there are none in the field of traffic, to the best of our knowledge, that introduces the process of missing data collection and the viability of the traffic data collected while also broadly covering the popularly used models of recent years. This has led to non-uniformity of the terms used in missing data imputation, limited research in areas where datasets are not available, and a narrowed view of the methods used for data imputation. Hence, this paper aims to standardize the terms used in missing data classifications, look into the limitations of using available public or private datasets for urban traffic research, and discuss popular statistical and data-driven methods used by recent AI and ITS papers. It was found that tensor decomposition-based methods are the most popular for missing data imputation, followed by Generative Adversarial Networks and Graph Neural Networks, all of which rely on a large training dataset. Meanwhile, Probability Principle Component Analysis (PPCA) methods provide valuable insights via traffic analysis and are used for real-time traffic imputation. This paper also highlights the need for more efficient and reliable methods for traffic data collection, such as online APIs.https://ieeexplore.ieee.org/document/10091533/Intelligent transportation systemsartificial intelligencecommunication system operations and managementreviews |
| spellingShingle | Robin Kuok Cheong Chan Joanne Mun-Yee Lim Rajendran Parthiban Missing Traffic Data Imputation for Artificial Intelligence in Intelligent Transportation Systems: Review of Methods, Limitations, and Challenges IEEE Access Intelligent transportation systems artificial intelligence communication system operations and management reviews |
| title | Missing Traffic Data Imputation for Artificial Intelligence in Intelligent Transportation Systems: Review of Methods, Limitations, and Challenges |
| title_full | Missing Traffic Data Imputation for Artificial Intelligence in Intelligent Transportation Systems: Review of Methods, Limitations, and Challenges |
| title_fullStr | Missing Traffic Data Imputation for Artificial Intelligence in Intelligent Transportation Systems: Review of Methods, Limitations, and Challenges |
| title_full_unstemmed | Missing Traffic Data Imputation for Artificial Intelligence in Intelligent Transportation Systems: Review of Methods, Limitations, and Challenges |
| title_short | Missing Traffic Data Imputation for Artificial Intelligence in Intelligent Transportation Systems: Review of Methods, Limitations, and Challenges |
| title_sort | missing traffic data imputation for artificial intelligence in intelligent transportation systems review of methods limitations and challenges |
| topic | Intelligent transportation systems artificial intelligence communication system operations and management reviews |
| url | https://ieeexplore.ieee.org/document/10091533/ |
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