Classification of the Traffic Status Subcategory with ETC Gantry Data: An Improved Support Tensor Machine Approach
Accurate and reliable traffic state identification is the prerequisite for developing intelligent traffic programs. With the improvement of intelligent traffic control measures, the traffic state of some highways has gradually stabilized. The current research on traffic state identification needs to...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/2765937 |
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| _version_ | 1832547990206676992 |
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| author | Yan Zhao Wenqi Lu Yikang Rui Bin Ran |
| author_facet | Yan Zhao Wenqi Lu Yikang Rui Bin Ran |
| author_sort | Yan Zhao |
| collection | DOAJ |
| description | Accurate and reliable traffic state identification is the prerequisite for developing intelligent traffic programs. With the improvement of intelligent traffic control measures, the traffic state of some highways has gradually stabilized. The current research on traffic state identification needs to fully meet the highly informative intelligent traffic system and traffic state subcategory analysis. To fill the gap above, we propose an improved support tensor machine (STM) method based on self-training and multiclassification for traffic state subcategory identification (ISTM) with ETC gantry data. This paper takes the excellent application of the support vector machine (SVM) in traffic state identification as the starting point of method design and extends to the STM. The ETC gantry data are represented as a third-order tensor model. This paper utilizes the similarity among tensor samples to construct the kernel function and recognize the traffic states. We simplify STM calculation with a one-against-one model and a self-training idea. An optimal fit of the characteristics is supplied by maximizing inter-subcategory tensor block distances and minimizing intra-subcategory tensor block distances throughout a joint utilization of the STM and multiscale training theories. The experiment in this paper uses ETC gantry data from the Jingtai highway in Shandong Province, and the findings reveal that the ISTM has optimum values of 0.2578 and 0.3254 for the SumD and 0.1718 and 0.1901 for the DBI as compared to K-mean clustering and the SVM. The ISTM trains the traffic state subcategory classifiers with high accuracy and strong generalization ability. |
| format | Article |
| id | doaj-art-671da50d68e24fa5b21628fd1a7c2068 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-671da50d68e24fa5b21628fd1a7c20682025-02-03T06:42:40ZengWileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/2765937Classification of the Traffic Status Subcategory with ETC Gantry Data: An Improved Support Tensor Machine ApproachYan Zhao0Wenqi Lu1Yikang Rui2Bin Ran3School of TransportationSchool of TransportationSchool of TransportationSchool of TransportationAccurate and reliable traffic state identification is the prerequisite for developing intelligent traffic programs. With the improvement of intelligent traffic control measures, the traffic state of some highways has gradually stabilized. The current research on traffic state identification needs to fully meet the highly informative intelligent traffic system and traffic state subcategory analysis. To fill the gap above, we propose an improved support tensor machine (STM) method based on self-training and multiclassification for traffic state subcategory identification (ISTM) with ETC gantry data. This paper takes the excellent application of the support vector machine (SVM) in traffic state identification as the starting point of method design and extends to the STM. The ETC gantry data are represented as a third-order tensor model. This paper utilizes the similarity among tensor samples to construct the kernel function and recognize the traffic states. We simplify STM calculation with a one-against-one model and a self-training idea. An optimal fit of the characteristics is supplied by maximizing inter-subcategory tensor block distances and minimizing intra-subcategory tensor block distances throughout a joint utilization of the STM and multiscale training theories. The experiment in this paper uses ETC gantry data from the Jingtai highway in Shandong Province, and the findings reveal that the ISTM has optimum values of 0.2578 and 0.3254 for the SumD and 0.1718 and 0.1901 for the DBI as compared to K-mean clustering and the SVM. The ISTM trains the traffic state subcategory classifiers with high accuracy and strong generalization ability.http://dx.doi.org/10.1155/2023/2765937 |
| spellingShingle | Yan Zhao Wenqi Lu Yikang Rui Bin Ran Classification of the Traffic Status Subcategory with ETC Gantry Data: An Improved Support Tensor Machine Approach Journal of Advanced Transportation |
| title | Classification of the Traffic Status Subcategory with ETC Gantry Data: An Improved Support Tensor Machine Approach |
| title_full | Classification of the Traffic Status Subcategory with ETC Gantry Data: An Improved Support Tensor Machine Approach |
| title_fullStr | Classification of the Traffic Status Subcategory with ETC Gantry Data: An Improved Support Tensor Machine Approach |
| title_full_unstemmed | Classification of the Traffic Status Subcategory with ETC Gantry Data: An Improved Support Tensor Machine Approach |
| title_short | Classification of the Traffic Status Subcategory with ETC Gantry Data: An Improved Support Tensor Machine Approach |
| title_sort | classification of the traffic status subcategory with etc gantry data an improved support tensor machine approach |
| url | http://dx.doi.org/10.1155/2023/2765937 |
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