Learning generalizable trajectory representations from raw GPS traces remains difficult because the data is continuous, noisy, and irregularly sampled. Spatial tokenization is also challenging: fine grids yield sparse cells with weak embeddings, while coarse grids merge heterogeneous movement patterns into the same token. We present TrajTok, a trajectory encoder with a simple pretraining recipe for transferable trajectory embeddings. TrajTok first learns a multi-resolution hexagonal cell partition from the spatial distribution of GPS points, converting noisy GPS sequences into discrete cell tokens. To capture both geometry and kinematics, it uses a factorized transformer encoder with early per-modality self-attention blocks, cross-attention fusion layers, and spatiotemporal rotary position embeddings, ST-RoPE, to encode where and when each token occurs. TrajTok is pretrained with masked-token modeling that recovers both geometric structure and kinematic patterns from partial trajectory observations. On the Porto dataset, a frozen TrajTok encoder with lightweight task adapters achieves strong performance across trajectory similarity search, classification, estimated time of arrival, and full travel-time regression, outperforming multiple task-specific methods. The same frozen encoder supports both geometry-dominated and kinematics-dominated tasks, suggesting that TrajTok learns transferable trajectory structure rather than task-specific shortcuts. These results indicate that learned multi-resolution spatial tokenization combined with masked-token pretraining is a promising direction for general-purpose trajectory foundation models.

翻译：从原始GPS轨迹数据中学习可泛化的轨迹表示仍然困难，因为这类数据具有连续性、噪声和采样不规则性。空间标记化同样面临挑战：细粒度网格会产生稀疏单元和弱嵌入，而粗粒度网格则会将异质性运动模式合并到同一标记中。本文提出TrajTok——一种带有简单预训练策略的轨迹编码器，用于生成可迁移的轨迹嵌入。TrajTok首先根据GPS点的空间分布学习一个多分辨率六边形网格划分，将含噪的GPS序列转化为离散的网格标记。为同时捕捉几何与运动学特征，它采用分解式Transformer编码器，包含早期按模态划分的自注意力模块、交叉注意力融合层，以及时空旋转位置编码（ST-RoPE），以编码每个标记出现的位置和时间。TrajTok通过掩码标记建模进行预训练，从部分轨迹观测中同时恢复几何结构和运动学模式。在Porto数据集上，冻结的TrajTok编码器配合轻量任务适配器，在轨迹相似性搜索、分类、预计到达时间和全程旅行时间回归等任务中均取得优异性能，超越多个任务专用方法。同一冻结编码器既能支持以几何为主导的任务，也能支持以运动学为主导的任务，表明TrajTok学习的是可迁移的轨迹结构而非任务特定的捷径。这些结果表明，将学习的多分辨率空间标记化与掩码标记预训练相结合，是构建通用轨迹基础模型的可行方向。