The widespread adoption of location-based services has led to the generation of vast amounts of mobility data, providing significant opportunities to model user movement dynamics within urban environments. Recent advancements have focused on adapting Large Language Models (LLMs) for mobility analytics. However, existing methods face two primary limitations: inadequate semantic representation of locations (i.e., discrete IDs) and insufficient modeling of mobility signals within LLMs (i.e., single templated instruction fine-tuning). To address these issues, we propose QT-Mob, a novel framework that significantly enhances LLMs for mobility analytics. QT-Mob introduces a location tokenization module that learns compact, semantically rich tokens to represent locations, preserving contextual information while ensuring compatibility with LLMs. Furthermore, QT-Mob incorporates a series of complementary fine-tuning objectives that align the learned tokens with the internal representations in LLMs, improving the model's comprehension of sequential movement patterns and location semantics. The proposed QT-Mob framework not only enhances LLMs' ability to interpret mobility data but also provides a more generalizable approach for various mobility analytics tasks. Experiments on three real-world dataset demonstrate the superior performance in both next-location prediction and mobility recovery tasks, outperforming existing deep learning and LLM-based methods.

Traffic states typically exhibit regular spatio-temporal patterns that support accurate prediction. However, traffic accidents can disrupt these patterns and introduce irregular fluctuations that challenge forecasting models. Previous studies have focused on predicting accident hotspots, risks, or traffic states using simple accident features. Yet, because traffic accidents are infrequent and exhibit spatio-temporal biases relative to collected traffic sensor data, they may significantly or only slightly affect traffic states, posing challenges for accurate prediction. In this study, we propose a large language model (LLM) enhanced Causality-aware Traffic accidents post-effects prediction (LLeCaT) to uncover the causal relationship between traffic states and accidents. Our model integrates fine-tuned LLMs to extract semantic information from accident records and estimate their effects on future traffic states following their occurrence. To validate the effectiveness of our proposed method, we collected and processed publicly available anonymized traffic accident records, traffic state datasets, and environmental information. We conducted extensive experiments demonstrating its superiority over all baselines. Additional ablation studies and case analyses further confirm the validity of the estimated causal effects.