Trajectory representation learning (TRL) aims to encode raw trajectory data into low-dimensional embeddings for downstream tasks such as travel time estimation, mobility prediction, and trajectory similarity analysis. From a behavioral perspective, a trajectory reflects a sequence of route choices within an urban environment. However, most existing TRL methods ignore this underlying decision-making process and instead treat trajectories as static, passive spatiotemporal sequences, thereby limiting the semantic richness of the learned representations. To bridge this gap, we propose CORE, a TRL framework that integrates context-aware route choice semantics into trajectory embeddings. CORE first incorporates a multi-granular Environment Perception Module, which leverages large language models (LLMs) to distill environmental semantics from point of interest (POI) distributions, thereby constructing a context-enriched road network. Building upon this backbone, CORE employs a Route Choice Encoder with a mixture-of-experts (MoE) architecture, which captures route choice patterns by jointly leveraging the context-enriched road network and navigational factors. Finally, a Transformer encoder aggregates the route-choice-aware representations into a global trajectory embedding. Extensive experiments on 4 real-world datasets across 6 downstream tasks demonstrate that CORE consistently outperforms 12 state-of-the-art TRL methods, achieving an average improvement of 9.79% over the best-performing baseline. Our code is available at https://github.com/caoji2001/CORE.

翻译：轨迹表示学习（TRL）旨在将原始轨迹数据编码为低维嵌入，以用于下游任务，如行程时间估计、移动性预测和轨迹相似性分析。从行为视角看，轨迹反映了城市环境中的一系列路径选择。然而，现有的大多数TRL方法忽略了这一潜在的决策过程，而将轨迹视为静态、被动的时空序列，从而限制了学习表示的语义丰富性。为弥合这一差距，我们提出了CORE，一个将上下文感知的路径选择语义整合到轨迹嵌入中的TRL框架。CORE首先引入了一个多粒度环境感知模块，该模块利用大型语言模型（LLMs）从兴趣点（POI）分布中提取环境语义，从而构建一个上下文增强的道路网络。基于此骨干结构，CORE采用了一个具有专家混合（MoE）架构的路径选择编码器，通过联合利用上下文增强的道路网络和导航因素来捕获路径选择模式。最后，一个Transformer编码器将这些路径选择感知的表示聚合为全局轨迹嵌入。在4个真实世界数据集上对6个下游任务进行的广泛实验表明，CORE始终优于12种最先进的TRL方法，相比性能最佳的基线平均提升了9.79%。我们的代码可在https://github.com/caoji2001/CORE获取。