Vehicle movement is frequently captured in the form of trajectories, i.e., sequences of timestamped locations. Numerous methods exist that target different tasks involving trajectories such as travel-time estimation, trajectory recovery, and trajectory prediction. However, most methods target only one specific task and cannot be applied universally. Existing efforts to create a universal trajectory model often involve adding prediction modules for adapting to different tasks, while also struggle with incomplete or sparse trajectories. To address these shortcomings, we propose the Universal Vehicle Trajectory Model (UVTM) designed to support different tasks based on incomplete or sparse trajectories without the need for retraining or extra prediction modules. To addresses task adaptability on incomplete trajectories, UVTM divide the spatio-temporal features of trajectories into three distinct domains. Each domain can be masked and generated independently to suit the input and output needs of specific tasks. To handle sparse trajectories effectively, UVTM is pre-trained by reconstructing densely sampled trajectories from sparsely sampled ones, allowing it to extract detailed spatio-temporal information from sparse trajectories. Experiments involving three representative trajectory-related tasks on two real-world vehicle trajectory datasets provide insight into the intended properties performance of UVTM and offer evidence that UVTM is capable of meeting its objectives.

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