Trajectory Generation of Ultra-Low-Frequency Travel Routes in Large-Scale Complex Road Networks
Trajectory generation can help predict the future road network state and properly deal with the privacy issues of trajectory data usage. To solve the problem that routes with very few journeys (ultra-low-frequency journey routes) are difficult to generate in the large-scale complex road network scen...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Systems |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-8954/11/2/61 |
| _version_ | 1827755382152364032 |
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| author | Jun Li Wenting Zhao |
| author_facet | Jun Li Wenting Zhao |
| author_sort | Jun Li |
| collection | DOAJ |
| description | Trajectory generation can help predict the future road network state and properly deal with the privacy issues of trajectory data usage. To solve the problem that routes with very few journeys (ultra-low-frequency journey routes) are difficult to generate in the large-scale complex road network scenarios, the study designs a framework focusing on ultra-low-frequency route generation, ULF-TrajGAIL, and proposes an original trajectory-augmentation method called the combined expansion method. The specific original trajectory-augmentation method is determined by the pre-trajectory-generation experiment, and high-quality synthetic trajectories with higher diversity and similarity are output based on the final generation experiments which take the augmented trajectories as references. Based on the real trajectories of a complex road network in a region of Guangzhou, the quality of synthetic trajectories under different original trajectory-augmentation methods from the route, link and origin and destination pairs levels has been compared. The results show that the method can generate more ultra-low-frequency routes and help improve the overall diversity of routes and the similarity between routes and the number of journeys as well. |
| first_indexed | 2024-03-11T08:03:33Z |
| format | Article |
| id | doaj.art-1f6c318be1284b9faf076c8b5aa7d42f |
| institution | Directory Open Access Journal |
| issn | 2079-8954 |
| language | English |
| last_indexed | 2024-03-11T08:03:33Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj.art-1f6c318be1284b9faf076c8b5aa7d42f2023-11-16T23:35:10ZengMDPI AGSystems2079-89542023-01-011126110.3390/systems11020061Trajectory Generation of Ultra-Low-Frequency Travel Routes in Large-Scale Complex Road NetworksJun Li0Wenting Zhao1School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, ChinaSchool of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, ChinaTrajectory generation can help predict the future road network state and properly deal with the privacy issues of trajectory data usage. To solve the problem that routes with very few journeys (ultra-low-frequency journey routes) are difficult to generate in the large-scale complex road network scenarios, the study designs a framework focusing on ultra-low-frequency route generation, ULF-TrajGAIL, and proposes an original trajectory-augmentation method called the combined expansion method. The specific original trajectory-augmentation method is determined by the pre-trajectory-generation experiment, and high-quality synthetic trajectories with higher diversity and similarity are output based on the final generation experiments which take the augmented trajectories as references. Based on the real trajectories of a complex road network in a region of Guangzhou, the quality of synthetic trajectories under different original trajectory-augmentation methods from the route, link and origin and destination pairs levels has been compared. The results show that the method can generate more ultra-low-frequency routes and help improve the overall diversity of routes and the similarity between routes and the number of journeys as well.https://www.mdpi.com/2079-8954/11/2/61vehicle trajectory generationgenerative modelimbalance learningdata augmentation |
| spellingShingle | Jun Li Wenting Zhao Trajectory Generation of Ultra-Low-Frequency Travel Routes in Large-Scale Complex Road Networks Systems vehicle trajectory generation generative model imbalance learning data augmentation |
| title | Trajectory Generation of Ultra-Low-Frequency Travel Routes in Large-Scale Complex Road Networks |
| title_full | Trajectory Generation of Ultra-Low-Frequency Travel Routes in Large-Scale Complex Road Networks |
| title_fullStr | Trajectory Generation of Ultra-Low-Frequency Travel Routes in Large-Scale Complex Road Networks |
| title_full_unstemmed | Trajectory Generation of Ultra-Low-Frequency Travel Routes in Large-Scale Complex Road Networks |
| title_short | Trajectory Generation of Ultra-Low-Frequency Travel Routes in Large-Scale Complex Road Networks |
| title_sort | trajectory generation of ultra low frequency travel routes in large scale complex road networks |
| topic | vehicle trajectory generation generative model imbalance learning data augmentation |
| url | https://www.mdpi.com/2079-8954/11/2/61 |
| work_keys_str_mv | AT junli trajectorygenerationofultralowfrequencytravelroutesinlargescalecomplexroadnetworks AT wentingzhao trajectorygenerationofultralowfrequencytravelroutesinlargescalecomplexroadnetworks |