Spatial—Temporal Traffic Flow Data Restoration and Prediction Method Based on the Tensor Decomposition
As an important part of urban big data, traffic flow data play a critical role in traffic management and emergency response. Traffic flow data contain multi-mode characteristics, which need to be deeply mined. To make full use of multi-mode characteristics, we use a 3-order tensor to represent the t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/19/9220 |
| _version_ | 1827680909901430784 |
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| author | Jiahe Yan Honghui Li Yanhui Bai Yingli Lin |
| author_facet | Jiahe Yan Honghui Li Yanhui Bai Yingli Lin |
| author_sort | Jiahe Yan |
| collection | DOAJ |
| description | As an important part of urban big data, traffic flow data play a critical role in traffic management and emergency response. Traffic flow data contain multi-mode characteristics, which need to be deeply mined. To make full use of multi-mode characteristics, we use a 3-order tensor to represent the traffic flow data, considering “temporal-spatial-periodic” characteristics. To recover the missing data of traffic flow, we propose the Missing Data Completion Algorithm Based on Residual Value Tensor Decomposition (MDCA-RVTD), which combines linear regression, univariate spline, and CP decomposition. Then, we predict the future traffic flow data by using the proposed Traffic Flow Prediction Algorithm Based on Data Completion Strategy (TFPA-DCS). The experimental results show that recovering the missing data is helpful in improving the prediction accuracy. Additionally, the prediction accuracy of the proposed Algorithm is better than gray model and traditional tensor CP decomposition method. |
| first_indexed | 2024-03-10T07:05:58Z |
| format | Article |
| id | doaj.art-aa1fd862da174abba186319628eea789 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T07:05:58Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-aa1fd862da174abba186319628eea7892023-11-22T15:49:30ZengMDPI AGApplied Sciences2076-34172021-10-011119922010.3390/app11199220Spatial—Temporal Traffic Flow Data Restoration and Prediction Method Based on the Tensor DecompositionJiahe Yan0Honghui Li1Yanhui Bai2Yingli Lin3School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, ChinaAs an important part of urban big data, traffic flow data play a critical role in traffic management and emergency response. Traffic flow data contain multi-mode characteristics, which need to be deeply mined. To make full use of multi-mode characteristics, we use a 3-order tensor to represent the traffic flow data, considering “temporal-spatial-periodic” characteristics. To recover the missing data of traffic flow, we propose the Missing Data Completion Algorithm Based on Residual Value Tensor Decomposition (MDCA-RVTD), which combines linear regression, univariate spline, and CP decomposition. Then, we predict the future traffic flow data by using the proposed Traffic Flow Prediction Algorithm Based on Data Completion Strategy (TFPA-DCS). The experimental results show that recovering the missing data is helpful in improving the prediction accuracy. Additionally, the prediction accuracy of the proposed Algorithm is better than gray model and traditional tensor CP decomposition method.https://www.mdpi.com/2076-3417/11/19/9220urban big datatraffic flow predictiontensor CP decomposition |
| spellingShingle | Jiahe Yan Honghui Li Yanhui Bai Yingli Lin Spatial—Temporal Traffic Flow Data Restoration and Prediction Method Based on the Tensor Decomposition Applied Sciences urban big data traffic flow prediction tensor CP decomposition |
| title | Spatial—Temporal Traffic Flow Data Restoration and Prediction Method Based on the Tensor Decomposition |
| title_full | Spatial—Temporal Traffic Flow Data Restoration and Prediction Method Based on the Tensor Decomposition |
| title_fullStr | Spatial—Temporal Traffic Flow Data Restoration and Prediction Method Based on the Tensor Decomposition |
| title_full_unstemmed | Spatial—Temporal Traffic Flow Data Restoration and Prediction Method Based on the Tensor Decomposition |
| title_short | Spatial—Temporal Traffic Flow Data Restoration and Prediction Method Based on the Tensor Decomposition |
| title_sort | spatial temporal traffic flow data restoration and prediction method based on the tensor decomposition |
| topic | urban big data traffic flow prediction tensor CP decomposition |
| url | https://www.mdpi.com/2076-3417/11/19/9220 |
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