<b>How do Large Language Models Understand Trajectory Data? Insights from Various Trajectory Formats</b><b>and Response Strategies for Transportation Mode Detection</b>

The effectiveness of large language models (LLMs) in transportation mode detection remains underexplored, creating a significant research gap in understanding how these models process trajectory data. This study uses the Geolife dataset to investigate the ability of pre-trained and fine-tuned LLMs to detect transportation modes across 14 trajectory formats, categorized into overview information, coordinate-based, and spatial encoding. Meanwhile , two response strategies are compared: direct answer and Chain-of-Thought (CoT) reasoning. The results show that fine-tuning significantly enhances the classification performance for all trajectory formats. Among the evaluated formats, the coordinate-based format with timestamps achieves the highest accuracy of 85.2% after fine-tuning using the direct answer strategy. The direct answer strategy proves to be more effective than the CoT strategy, reaching an average 49.0% improvement in accuracy via fine-tuning. Additionally, the model exhibits systematic misclassification patterns, reflecting challenges in distinguishing between transportation modes with similar movement characteristics. Furthermore, our analysis reveals that hallucinations are prevalent in CoT responses, particularly of the types of input-conflicting hallucinations and factual inaccuracies, which increase the likelihood of misclassification. These findings highlight the potential of LLMs in transportation mode detection while emphasizing the need for enhanced trajectory formats, improved response strategies, and strategies to mitigate hallucinations.大语言模型（Large Language Models，LLMs）在交通方式识别领域的应用有效性仍未得到充分探索，这在理解此类模型如何处理轨迹数据方面留下了显著的研究空白。本研究采用Geolife数据集，针对14种轨迹格式下的交通方式识别任务，探究预训练与微调后的大语言模型的识别能力，这些轨迹格式可分为概览信息类、基于坐标类与空间编码类。同时，本研究对比了两种响应策略：直接回答与思维链（Chain-of-Thought，CoT）推理。实验结果表明，微调可显著提升所有轨迹格式下的分类性能。在所评估的格式中，采用直接回答策略进行微调后，带时间戳的基于坐标类格式取得了最高的85.2%准确率。研究证实，直接回答策略的效果优于思维链策略，经微调后准确率平均提升49.0%。此外，模型存在系统性的误分类模式，反映出区分运动特征相似的交通方式所面临的挑战。进一步分析显示，思维链响应中普遍存在幻觉问题，尤以输入冲突型幻觉与事实不准确问题为主，这类问题会提升误分类的概率。上述研究结果既凸显了大语言模型在交通方式识别任务中的应用潜力，同时也强调了优化轨迹格式、改进响应策略以及缓解幻觉问题的必要性。