A prediction method of missing vehicle position information based on least square support vector machine
The continuous development of VANET has accelerated the development of V2X communication. In the DSRC communication mode of VANET, the location information of the vehicles is interfered by factors such as high-density broadcasting and electromagnetic radiation, which can lead to the loss of the orig...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2021-01-01
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| Series: | Sustainable Operations and Computers |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266641272100012X |
| _version_ | 1797976459744116736 |
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| author | Peng DU Xiaoqi MA Zhuanping WANG Yuanfu MO Peng PENG |
| author_facet | Peng DU Xiaoqi MA Zhuanping WANG Yuanfu MO Peng PENG |
| author_sort | Peng DU |
| collection | DOAJ |
| description | The continuous development of VANET has accelerated the development of V2X communication. In the DSRC communication mode of VANET, the location information of the vehicles is interfered by factors such as high-density broadcasting and electromagnetic radiation, which can lead to the loss of the original vehicle information data collected by GPS easily. To solve it, this paper proposed the Least Squared SVM based Beacon Data Complete Algorithm. Unlike previous studies that historical trends of vehicle operation were mainly used to predict vehicle location., this method attempts to find a function, which is used to establish the relationship between the lost value and the past value of the vehicle. On this basis, a nonlinear function approximation strategy is used to predict the position of the missing vehicle. Part of the original data was lost artificially to complete checking calculation and to verify the effectiveness of it. The results show that the average relative error between the complemented vehicle position data and the real data is 0.45% and the maximum absolute relative error is 8.25%. This method has the advantage of not needing to extract historical trend data and high calculation accuracy compared with the methods such as PWHOG algorithm, difference matrix, and moving average data preprocessing. It is suitable for real-time acquisition of vehicle position of VANET and can reduce the complexity of detection time. |
| first_indexed | 2024-04-11T04:51:13Z |
| format | Article |
| id | doaj.art-910c6805ecbf418496d65108dfa1c1f8 |
| institution | Directory Open Access Journal |
| issn | 2666-4127 |
| language | English |
| last_indexed | 2024-04-11T04:51:13Z |
| publishDate | 2021-01-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Sustainable Operations and Computers |
| spelling | doaj.art-910c6805ecbf418496d65108dfa1c1f82022-12-27T04:37:27ZengKeAi Communications Co. Ltd.Sustainable Operations and Computers2666-41272021-01-0123035A prediction method of missing vehicle position information based on least square support vector machinePeng DU0Xiaoqi MA1Zhuanping WANG2Yuanfu MO3Peng PENG4Shandong Expressway Co. Ltd, Jinan 250014, ChinaSchool of Transportation, Jilin University, Changchun 130022, China; Corresponding author.Shandong Expressway Co. Ltd, Jinan 250014, ChinaJilin Key Laboratory of Road Traffic, Changchun 130022, ChinaShandong Expressway Co. Ltd, Jinan 250014, ChinaThe continuous development of VANET has accelerated the development of V2X communication. In the DSRC communication mode of VANET, the location information of the vehicles is interfered by factors such as high-density broadcasting and electromagnetic radiation, which can lead to the loss of the original vehicle information data collected by GPS easily. To solve it, this paper proposed the Least Squared SVM based Beacon Data Complete Algorithm. Unlike previous studies that historical trends of vehicle operation were mainly used to predict vehicle location., this method attempts to find a function, which is used to establish the relationship between the lost value and the past value of the vehicle. On this basis, a nonlinear function approximation strategy is used to predict the position of the missing vehicle. Part of the original data was lost artificially to complete checking calculation and to verify the effectiveness of it. The results show that the average relative error between the complemented vehicle position data and the real data is 0.45% and the maximum absolute relative error is 8.25%. This method has the advantage of not needing to extract historical trend data and high calculation accuracy compared with the methods such as PWHOG algorithm, difference matrix, and moving average data preprocessing. It is suitable for real-time acquisition of vehicle position of VANET and can reduce the complexity of detection time.http://www.sciencedirect.com/science/article/pii/S266641272100012XIntelligent transportationVANETVehicle position missing predictionLeast squares support vector machine |
| spellingShingle | Peng DU Xiaoqi MA Zhuanping WANG Yuanfu MO Peng PENG A prediction method of missing vehicle position information based on least square support vector machine Sustainable Operations and Computers Intelligent transportation VANET Vehicle position missing prediction Least squares support vector machine |
| title | A prediction method of missing vehicle position information based on least square support vector machine |
| title_full | A prediction method of missing vehicle position information based on least square support vector machine |
| title_fullStr | A prediction method of missing vehicle position information based on least square support vector machine |
| title_full_unstemmed | A prediction method of missing vehicle position information based on least square support vector machine |
| title_short | A prediction method of missing vehicle position information based on least square support vector machine |
| title_sort | prediction method of missing vehicle position information based on least square support vector machine |
| topic | Intelligent transportation VANET Vehicle position missing prediction Least squares support vector machine |
| url | http://www.sciencedirect.com/science/article/pii/S266641272100012X |
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