TrajLM: Transferable Mobility Pattern Distillation for Unified Trajectory Learning in Sparse Road Networks

Spatio-temporal trajectory data mining plays a pivotal role in understanding and predicting object movements across space and time, impacting crucial location-based applications such as urban traffic optimization, human mobility analysis, and autonomous vehicle navigation. Although traditional methods effectively capture local trajectory patterns, they struggle with the semantic understanding required for robust representation of diverse traffic scenarios due to their limited ability to incorporate global contextual semantics. Recent advances in large language models (LLMs) have shown promise in capturing semantic relationships from extensive data; however, they face difficulties adapting their powerful semantic learning capabilities to sparse regions where only a few trajectories are traversed because of uneven geographical coverage. To bridge this gap, we propose TrajLM, a novel model that distills transferable mobility patterns in sparse road networks for unified trajectory representation via large language models. Our approach facilitates deep collaboration between dense and sparse trajectory regions, leveraging mutual knowledge distillation to mitigate the sparsity challenge and enhance semantic representation. Moreover, by incorporating spatio-temporal dynamics, our model enables LLMs to learn time-varying movement representations, thereby improving mobility understanding. Extensive experiments demonstrate that our model significantly outperforms state-of-the-art competitors across three downstream tasks, achieving an average improvement of 29.51%.