Vehicle Trajectory Generation Based on Generation Adversarial Network
With the development of networked vehicles, location information-based transportation systems have proven to provide significant benefits. However, the exposure of vehicle location information also raises important privacy issues. Current typical methods for protecting vehicle location privacy prote...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-06-01
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| Series: | Journal of Highway and Transportation Research and Development |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/HTRD.2024.9480017 |
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| author | Zhonghe He Renchi Shao Sijia Xiang |
| author_facet | Zhonghe He Renchi Shao Sijia Xiang |
| author_sort | Zhonghe He |
| collection | DOAJ |
| description | With the development of networked vehicles, location information-based transportation systems have proven to provide significant benefits. However, the exposure of vehicle location information also raises important privacy issues. Current typical methods for protecting vehicle location privacy protection methods such as anonymity and pseudonymity, still carry the risk of the vehicle being tracked, leading to data security issues. This paper proposes a kind of vehicle trajectory generation algorithm based on Generative Adversarial Networks (GAN). The algorithm utilizes vehicle movement trajectory data to train both the discriminator and generator models to generate virtual trajectory data that matches the distribution of real trajectory data. Therefore, virtual trajectory data can obscure vehicle information, addressing the privacy concerns associated with moving trajectory data and enhancing the security of applications. In this paper, the vehicle travel time of sample trajectory data and virtual trajectory data is used as indicators for statistical analysis. The experiment demonstrated that the cumulative probability distribution of travel time for the sample data and virtual data passed the Kolmogorov-Smirnov (K-S) test at permeabilities ranging from 10% to 100% and at significance levels of 0.01 and 0.05. Both datasets accepted the hypothesis that they originate from the same distribution. The reliability of the proposed method for generating virtual trajectories has been demonstrated. |
| format | Article |
| id | doaj-art-5839fbc52381410e92bf0602a51ee8bf |
| institution | OA Journals |
| issn | 2095-6215 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Journal of Highway and Transportation Research and Development |
| spelling | doaj-art-5839fbc52381410e92bf0602a51ee8bf2025-08-20T02:05:07ZengTsinghua University PressJournal of Highway and Transportation Research and Development2095-62152024-06-01182828810.26599/HTRD.2024.9480017Vehicle Trajectory Generation Based on Generation Adversarial NetworkZhonghe He0Renchi Shao1Sijia Xiang2School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, ChinaSchool of Electrical and Control Engineering, North China University of Technology, Beijing 100144, ChinaSchool of Electrical and Control Engineering, North China University of Technology, Beijing 100144, ChinaWith the development of networked vehicles, location information-based transportation systems have proven to provide significant benefits. However, the exposure of vehicle location information also raises important privacy issues. Current typical methods for protecting vehicle location privacy protection methods such as anonymity and pseudonymity, still carry the risk of the vehicle being tracked, leading to data security issues. This paper proposes a kind of vehicle trajectory generation algorithm based on Generative Adversarial Networks (GAN). The algorithm utilizes vehicle movement trajectory data to train both the discriminator and generator models to generate virtual trajectory data that matches the distribution of real trajectory data. Therefore, virtual trajectory data can obscure vehicle information, addressing the privacy concerns associated with moving trajectory data and enhancing the security of applications. In this paper, the vehicle travel time of sample trajectory data and virtual trajectory data is used as indicators for statistical analysis. The experiment demonstrated that the cumulative probability distribution of travel time for the sample data and virtual data passed the Kolmogorov-Smirnov (K-S) test at permeabilities ranging from 10% to 100% and at significance levels of 0.01 and 0.05. Both datasets accepted the hypothesis that they originate from the same distribution. The reliability of the proposed method for generating virtual trajectories has been demonstrated.https://www.sciopen.com/article/10.26599/HTRD.2024.9480017traffic engineeringvehicle location privacy protectiontraffic data securitygenerative adversarial networkvirtual trajectory |
| spellingShingle | Zhonghe He Renchi Shao Sijia Xiang Vehicle Trajectory Generation Based on Generation Adversarial Network Journal of Highway and Transportation Research and Development traffic engineering vehicle location privacy protection traffic data security generative adversarial network virtual trajectory |
| title | Vehicle Trajectory Generation Based on Generation Adversarial Network |
| title_full | Vehicle Trajectory Generation Based on Generation Adversarial Network |
| title_fullStr | Vehicle Trajectory Generation Based on Generation Adversarial Network |
| title_full_unstemmed | Vehicle Trajectory Generation Based on Generation Adversarial Network |
| title_short | Vehicle Trajectory Generation Based on Generation Adversarial Network |
| title_sort | vehicle trajectory generation based on generation adversarial network |
| topic | traffic engineering vehicle location privacy protection traffic data security generative adversarial network virtual trajectory |
| url | https://www.sciopen.com/article/10.26599/HTRD.2024.9480017 |
| work_keys_str_mv | AT zhonghehe vehicletrajectorygenerationbasedongenerationadversarialnetwork AT renchishao vehicletrajectorygenerationbasedongenerationadversarialnetwork AT sijiaxiang vehicletrajectorygenerationbasedongenerationadversarialnetwork |