标题： TrajSceneLLM：语义GPS轨迹分析的多模态视角 显示英文标题

标题： TrajSceneLLM: A Multimodal Perspective on Semantic GPS Trajectory Analysis

摘要： GPS轨迹数据揭示了人类移动和城市动态的宝贵模式，支持多种空间应用。 然而，传统方法往往难以提取深层语义表示并结合上下文地图信息。 我们提出了TrajSceneLLM，这是一种增强GPS轨迹语义理解的多模态视角。 该框架集成了可视化地图图像（编码空间上下文）和通过LLM推理生成的文本描述（捕捉时间序列和移动动态）。 为每种模态生成单独的嵌入，然后将其连接起来生成具有丰富语义内容的轨迹场景嵌入，并进一步与简单的MLP分类器配对。 我们在旅行模式识别（TMI）任务上验证了所提出的框架，这是分析出行选择和理解移动行为的关键任务。 我们的实验表明，这些嵌入实现了显著的性能提升，突显了我们基于LLM的方法在捕捉深层时空依赖关系和减少对手工特征依赖方面的优势。 这种语义增强为地理空间人工智能的多样下游应用和未来研究带来了巨大潜力。 源代码和数据集可在以下网址公开获取：https://github.com/februarysea/TrajSceneLLM。 显示英文摘要

摘要： GPS trajectory data reveals valuable patterns of human mobility and urban dynamics, supporting a variety of spatial applications. However, traditional methods often struggle to extract deep semantic representations and incorporate contextual map information. We propose TrajSceneLLM, a multimodal perspective for enhancing semantic understanding of GPS trajectories. The framework integrates visualized map images (encoding spatial context) and textual descriptions generated through LLM reasoning (capturing temporal sequences and movement dynamics). Separate embeddings are generated for each modality and then concatenated to produce trajectory scene embeddings with rich semantic content which are further paired with a simple MLP classifier. We validate the proposed framework on Travel Mode Identification (TMI), a critical task for analyzing travel choices and understanding mobility behavior. Our experiments show that these embeddings achieve significant performance improvement, highlighting the advantage of our LLM-driven method in capturing deep spatio-temporal dependencies and reducing reliance on handcrafted features. This semantic enhancement promises significant potential for diverse downstream applications and future research in geospatial artificial intelligence. The source code and dataset are publicly available at: https://github.com/februarysea/TrajSceneLLM.