{"ID":2854717,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.14819","arxiv_id":"2510.14819","title":"Capturing Context-Aware Route Choice Semantics for Trajectory Representation Learning","abstract":"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.","short_abstract":"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 envir...","url_abs":"https://arxiv.org/abs/2510.14819","url_pdf":"https://arxiv.org/pdf/2510.14819v2","authors":"[\"Ji Cao\",\"Yu Wang\",\"Tongya Zheng\",\"Jie Song\",\"Qinghong Guo\",\"Zujie Ren\",\"Canghong Jin\",\"Gang Chen\",\"Mingli Song\"]","published":"2025-10-16T15:55:28Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.LG\"]","methods":"[\"Transformer\",\"Large Language Model\",\"Language Model\"]","has_code":false,"code_links":[{"ID":608187,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2854717,"paper_url":"https://arxiv.org/abs/2510.14819","paper_title":"Capturing Context-Aware Route Choice Semantics for Trajectory Representation Learning","repo_url":"https://github.com/caoji2001/CORE","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}