International audience

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.

In this paper, we examine the properties of the radial distance which has been used as a tool to compare the shape of simple surfacic objects. We give a rigorous definition of the radial distance and derive its theoretical properties, and in particular under which conditions it satisfies the distance properties. We show how the computation of the radial distance can be implemented in practice and made faster by the use of an analytical formula and a Fast Fourier Transform. Finally, we conduct experiments to measure how the radial distance is impacted by perturbation and generalization and we give abacuses and thresholds to deduce when buildings are likely to be homologous or non-homologous given their radial distance.