Diff-RNTraj: A Structure-aware Diffusion Model for Road Network-constrained Trajectory Generation
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914946980249600 |
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| author | Wei, Tonglong Lin, Youfang Guo, Shengnan Lin, Yan Huang, Yiheng Xiang, Chenyang Bai, Yuqing Wan, Huaiyu |
| author_facet | Wei, Tonglong Lin, Youfang Guo, Shengnan Lin, Yan Huang, Yiheng Xiang, Chenyang Bai, Yuqing Wan, Huaiyu |
| contents | Trajectory data is essential for various applications as it records the movement of vehicles. However, publicly available trajectory datasets remain limited in scale due to privacy concerns, which hinders the development of trajectory data mining and trajectory-based applications. To address this issue, some methods for generating synthetic trajectories have been proposed to expand the scale of the dataset. However, all existing methods generate trajectories in the geographical coordinate system, which poses two limitations for their utilization in practical applications: 1) the inability to ensure that the generated trajectories are constrained on the road. 2) the lack of road-related information. In this paper, we propose a new problem to meet the practical application need, \emph{i.e.}, road network-constrained trajectory (RNTraj) generation, which can directly generate trajectories on the road network with road-related information. RNTraj is a hybrid type of data, in which each point is represented by a discrete road segment and a continuous moving rate. To generate RNTraj, we design a diffusion model called Diff-RNTraj. This model can effectively handle the hybrid RNTraj using a continuous diffusion framework by incorporating a pre-training strategy to embed hybrid RNTraj into continuous representations. During the sampling stage, a RNTraj decoder is designed to map the continuous representation generated by the diffusion model back to the hybrid RNTraj format. Furthermore, Diff-RNTraj introduces a novel loss function to enhance the spatial validity of the generated trajectories. Extensive experiments conducted on two real-world trajectory datasets demonstrate the effectiveness of the proposed model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_07369 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Diff-RNTraj: A Structure-aware Diffusion Model for Road Network-constrained Trajectory Generation Wei, Tonglong Lin, Youfang Guo, Shengnan Lin, Yan Huang, Yiheng Xiang, Chenyang Bai, Yuqing Wan, Huaiyu Machine Learning Trajectory data is essential for various applications as it records the movement of vehicles. However, publicly available trajectory datasets remain limited in scale due to privacy concerns, which hinders the development of trajectory data mining and trajectory-based applications. To address this issue, some methods for generating synthetic trajectories have been proposed to expand the scale of the dataset. However, all existing methods generate trajectories in the geographical coordinate system, which poses two limitations for their utilization in practical applications: 1) the inability to ensure that the generated trajectories are constrained on the road. 2) the lack of road-related information. In this paper, we propose a new problem to meet the practical application need, \emph{i.e.}, road network-constrained trajectory (RNTraj) generation, which can directly generate trajectories on the road network with road-related information. RNTraj is a hybrid type of data, in which each point is represented by a discrete road segment and a continuous moving rate. To generate RNTraj, we design a diffusion model called Diff-RNTraj. This model can effectively handle the hybrid RNTraj using a continuous diffusion framework by incorporating a pre-training strategy to embed hybrid RNTraj into continuous representations. During the sampling stage, a RNTraj decoder is designed to map the continuous representation generated by the diffusion model back to the hybrid RNTraj format. Furthermore, Diff-RNTraj introduces a novel loss function to enhance the spatial validity of the generated trajectories. Extensive experiments conducted on two real-world trajectory datasets demonstrate the effectiveness of the proposed model. |
| title | Diff-RNTraj: A Structure-aware Diffusion Model for Road Network-constrained Trajectory Generation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.07369 |