UVTM: Universal Vehicle Trajectory Modeling with ST Feature Domain Generation

• URL: http://arxiv.org/abs/2402.07232v4
• Date: Tue, 13 May 2025 08:16:03 GMT
• Title: UVTM: Universal Vehicle Trajectory Modeling with ST Feature Domain Generation
• Authors: Yan Lin, Jilin Hu, Shengnan Guo, Bin Yang, Christian S. Jensen, Youfang Lin, Huaiyu Wan,
• Abstract summary: A universal vehicle trajectory model could be applied to different tasks, removing the need to maintain multiple specialized models.<n>We propose the Universal Vehicle Trajectory Model (UVTM), which can effectively adapt to different tasks without excessive retraining.<n>UVTM is pre-trained by reconstructing dense, feature-complete trajectories from sparse, feature-incomplete counterparts.
• Score: 34.918489559139715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vehicle movement is frequently captured in the form of GPS trajectories, i.e., sequences of timestamped GPS locations. Such data is widely used for various tasks such as travel-time estimation, trajectory recovery, and trajectory prediction. A universal vehicle trajectory model could be applied to different tasks, removing the need to maintain multiple specialized models, thereby reducing computational and storage costs. However, creating such a model is challenging when the integrity of trajectory features is compromised, i.e., in scenarios where only partial features are available or the trajectories are sparse. To address these challenges, we propose the Universal Vehicle Trajectory Model (UVTM), which can effectively adapt to different tasks without excessive retraining. UVTM incorporates two specialized designs. First, it divides trajectory features into three distinct domains. Each domain can be masked and generated independently to accommodate tasks with only partially available features. Second, UVTM is pre-trained by reconstructing dense, feature-complete trajectories from sparse, feature-incomplete counterparts, enabling strong performance even when the integrity of trajectory features is compromised. Experiments involving four representative trajectory-related tasks on three real-world vehicle trajectory datasets provide insight into the performance of UVTM and offer evidence that it is capable of meeting its objectives.

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