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.

翻译：车辆运动常以轨迹形式（即带时间戳的位置序列）被采集。现有方法针对不同轨迹任务（如行程时间估计、轨迹恢复与轨迹预测）已取得进展，但多数方法仅面向单一任务，难以实现通用化。当前构建通用轨迹模型的尝试往往需要为适配不同任务添加预测模块，同时仍难以处理不完整或稀疏轨迹。为解决上述问题，本文提出通用车辆轨迹模型（UVTM），该模型无需重新训练或额外预测模块，即可基于不完整或稀疏轨迹支持多种任务。针对不完整轨迹的任务适配性，UVTM将轨迹的时空特征划分为三个独立域。每个域可独立进行掩码与生成，以匹配特定任务的输入输出需求。为有效处理稀疏轨迹，UVTM通过从稀疏采样轨迹重建密集采样轨迹进行预训练，从而能从稀疏轨迹中提取精细时空信息。基于两个真实车辆轨迹数据集的三项代表性轨迹任务实验，揭示了UVTM的预期性能特征，并证实其具备实现既定目标的能力。