Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems
The deployment of Electronic Toll Collection (ETC) gantry systems marks a transformative advancement in the journey toward an interconnected and intelligent highway traffic infrastructure. The integration of these systems signifies a leap forward in streamlining toll collection and minimizing enviro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/1/86 |
| _version_ | 1797358172529229824 |
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| author | Yikang Rui Yan Zhao Wenqi Lu Can Wang |
| author_facet | Yikang Rui Yan Zhao Wenqi Lu Can Wang |
| author_sort | Yikang Rui |
| collection | DOAJ |
| description | The deployment of Electronic Toll Collection (ETC) gantry systems marks a transformative advancement in the journey toward an interconnected and intelligent highway traffic infrastructure. The integration of these systems signifies a leap forward in streamlining toll collection and minimizing environmental impact through decreased idle times. To solve the problems of missing sensor data in an ETC gantry system with large volumes and insufficient traffic detection among ETC gantries, this study constructs a high-order tensor model based on the analysis of the high-dimensional, sparse, large-volume, and heterogeneous characteristics of ETC gantry data. In addition, a missing data completion method for the ETC gantry data is proposed based on an improved dynamic tensor flow model. This study approximates the decomposition of neighboring tensor blocks in the high-order tensor model of the ETC gantry data based on tensor Tucker decomposition and the Laplacian matrix. This method captures the correlations among space, time, and user information in the ETC gantry data. Case studies demonstrate that our method enhances ETC gantry data quality across various rates of missing data while also reducing computational complexity. For instance, at a less than 5% missing data rate, our approach reduced the RMSE for time vehicle distance by 0.0051, for traffic volume by 0.0056, and for interval speed by 0.0049 compared to the MATRIX method. These improvements not only indicate a potential for more precise traffic data analysis but also add value to the application of ETC systems and contribute to theoretical and practical advancements in the field. |
| first_indexed | 2024-03-08T14:57:03Z |
| format | Article |
| id | doaj.art-18dbecb5bfbc4c1f8a2edb8f7bbbc750 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T14:57:03Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-18dbecb5bfbc4c1f8a2edb8f7bbbc7502024-01-10T15:08:31ZengMDPI AGSensors1424-82202023-12-012418610.3390/s24010086Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry SystemsYikang Rui0Yan Zhao1Wenqi Lu2Can Wang3School of Transportation, Southeast University, Nanjing 211189, ChinaSchool of Transportation, Southeast University, Nanjing 211189, ChinaSchool of Transportation, Southeast University, Nanjing 211189, ChinaSchool of Transportation, Southeast University, Nanjing 211189, ChinaThe deployment of Electronic Toll Collection (ETC) gantry systems marks a transformative advancement in the journey toward an interconnected and intelligent highway traffic infrastructure. The integration of these systems signifies a leap forward in streamlining toll collection and minimizing environmental impact through decreased idle times. To solve the problems of missing sensor data in an ETC gantry system with large volumes and insufficient traffic detection among ETC gantries, this study constructs a high-order tensor model based on the analysis of the high-dimensional, sparse, large-volume, and heterogeneous characteristics of ETC gantry data. In addition, a missing data completion method for the ETC gantry data is proposed based on an improved dynamic tensor flow model. This study approximates the decomposition of neighboring tensor blocks in the high-order tensor model of the ETC gantry data based on tensor Tucker decomposition and the Laplacian matrix. This method captures the correlations among space, time, and user information in the ETC gantry data. Case studies demonstrate that our method enhances ETC gantry data quality across various rates of missing data while also reducing computational complexity. For instance, at a less than 5% missing data rate, our approach reduced the RMSE for time vehicle distance by 0.0051, for traffic volume by 0.0056, and for interval speed by 0.0049 compared to the MATRIX method. These improvements not only indicate a potential for more precise traffic data analysis but also add value to the application of ETC systems and contribute to theoretical and practical advancements in the field.https://www.mdpi.com/1424-8220/24/1/86tensor Tucker decompositiondynamic tensor modelingdata imputationdata integrity |
| spellingShingle | Yikang Rui Yan Zhao Wenqi Lu Can Wang Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems Sensors tensor Tucker decomposition dynamic tensor modeling data imputation data integrity |
| title | Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems |
| title_full | Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems |
| title_fullStr | Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems |
| title_full_unstemmed | Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems |
| title_short | Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems |
| title_sort | dynamic tensor modeling for missing data completion in electronic toll collection gantry systems |
| topic | tensor Tucker decomposition dynamic tensor modeling data imputation data integrity |
| url | https://www.mdpi.com/1424-8220/24/1/86 |
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