Harnessing Large Language Models for Predicting Mobility Modes

Understanding and classifying mobility modes, such as walking, cycling, driving, or public transport, is essential for sustainable urban planning and mobility behavior analysis. Traditional approaches rely on handcrafted features and machine learning models trained on GPS trajectory data. However, these methods require extensive data preparation and model training. In this work, we explore the potential of large language models (LLMs) as zero-shot predictors for transportation mode classification, eliminating the need for training data altogether. We propose a pipeline that transforms enriched trajectory segments into textual prompts, enabling LLMs to perform classification without task-specific pretraining. We benchmark the performance of a locally distilled 32B parameter LLM (DeepSeek Gwen) against standard machine learning baselines on the Geolife dataset. Preliminary results demonstrate that LLMs effectively capture semantic and contextual cues from trajectory-derived features, highlighting their promise for rapid, data-efficient transportation mode classification. Our work provides novel insights into leveraging LLMs in the mobility domain and identifies future opportunities for their integration.